Datasets:

kasnerz

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logic2text

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boston university terriers men 's ice hockey

https://en.wikipedia.org/wiki/Boston_University_Terriers_men%27s_ice_hockey

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12453414-5.html.csv

comparative

for boston university terriers men 's ice hockey , mark fidler scored 3 more goals than david sacco .

{'row_1': '8', 'row_2': '2', 'col': '3', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '3', 'bigger': 'row1'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'mark fidler'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to mark fidler .', 'tostr': 'filter_eq { all_rows ; player ; mark fidler }'}, 'goals'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; mark fidler } ; goals }', 'tointer': 'select the rows whose player record fuzzily matches to mark fidler . take the goals record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'david sacco'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to david sacco .', 'tostr': 'filter_eq { all_rows ; player ; david sacco }'}, 'goals'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; david sacco } ; goals }', 'tointer': 'select the rows whose player record fuzzily matches to david sacco . take the goals record of this row .'}], 'result': '3', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; mark fidler } ; goals } ; hop { filter_eq { all_rows ; player ; david sacco } ; goals } }'}, '3'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; mark fidler } ; goals } ; hop { filter_eq { all_rows ; player ; david sacco } ; goals } } ; 3 } = true', 'tointer': 'select the rows whose player record fuzzily matches to mark fidler . take the goals record of this row . select the rows whose player record fuzzily matches to david sacco . take the goals record of this row . the first record is 3 larger than the second record .'}

eq { diff { hop { filter_eq { all_rows ; player ; mark fidler } ; goals } ; hop { filter_eq { all_rows ; player ; david sacco } ; goals } } ; 3 } = true

select the rows whose player record fuzzily matches to mark fidler . take the goals record of this row . select the rows whose player record fuzzily matches to david sacco . take the goals record of this row . the first record is 3 larger than the second record .

6

6

{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'mark fidler_9': 9, 'goals_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'david sacco_13': 13, 'goals_14': 14, '3_15': 15}

{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'player_8': 'player', 'mark fidler_9': 'mark fidler', 'goals_10': 'goals', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'david sacco_13': 'david sacco', 'goals_14': 'goals', '3_15': '3'}

{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'mark fidler_9': [0], 'goals_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'david sacco_13': [1], 'goals_14': [3], '3_15': [5]}

['player', 'years', 'goals', 'assists', 'points']

[['john cullen', '1983 - 87', '98', '143', '241'], ['david sacco', '1989 - 93', '74', '143', '217'], ['chris drury', '1994 - 98', '113', '101', '214'], ['rick meagher', '1973 - 77', '90', '120', '210'], ['mike eruzione', '1973 - 77', '92', '116', '208'], ['shawn mceachern', '1988 - 91', '79', '107', '186'], ['david tomlinson', '1987 - 91', '77', '102', '179'], ['mark fidler', '1977 - 81', '77', '101', '178'], ['mike kelfer', '1985 - 89', '83', '89', '172'], ['mike hyndman', '1967 - 70', '52', '119', '171']]

list of airlines of singapore

https://en.wikipedia.org/wiki/List_of_airlines_of_Singapore

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15520072-1.html.csv

majority

for the airlines of singapore , most commenced operations after the year 2000 .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '2000', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'commenced operations', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the commenced operations records of all rows , most of them are greater than 2000 .', 'tostr': 'most_greater { all_rows ; commenced operations ; 2000 } = true'}

most_greater { all_rows ; commenced operations ; 2000 } = true

for the commenced operations records of all rows , most of them are greater than 2000 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'commenced operations_3': 3, '2000_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'commenced operations_3': 'commenced operations', '2000_4': '2000'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'commenced operations_3': [0], '2000_4': [0]}

['airlines', 'iata', 'icao', 'callsign', 'commenced operations']

[['jetstar asia airways', '3k', 'jsa', 'jetstar asia', '2004'], ['scoot', 'tz', 'sco', 'scooter', '2012'], ['silkair', 'mi', 'slk', 'silkair', '1976'], ['singapore airlines', 'sq', 'sia', 'singapore', '1947'], ['singapore airlines cargo', 'sq', 'sqc', 'singcargo', '2001'], ['tigerair', 'tr', 'tgw', 'go cat', '2003'], ['valuair', 'vf', 'vlu', 'valuair', '2004']]

2007 - 08 dallas stars season

https://en.wikipedia.org/wiki/2007%E2%80%9308_Dallas_Stars_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11801912-3.html.csv

aggregation

in the 2007-08 dallas stars season , 9 points were scored by dallas in the last two games of october .

{'scope': 'subset', 'col': '3', 'type': 'sum', 'result': '9', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': 'october 29'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'date', 'october 29'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; date ; october 29 }', 'tointer': 'select the rows whose date record is greater than or equal to october 29 .'}, 'score'], 'result': '9', 'ind': 1, 'tostr': 'sum { filter_greater_eq { all_rows ; date ; october 29 } ; score }'}, '9'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater_eq { all_rows ; date ; october 29 } ; score } ; 9 } = true', 'tointer': 'select the rows whose date record is greater than or equal to october 29 . the sum of the score record of these rows is 9 .'}

round_eq { sum { filter_greater_eq { all_rows ; date ; october 29 } ; score } ; 9 } = true

select the rows whose date record is greater than or equal to october 29 . the sum of the score record of these rows is 9 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, 'october 29_6': 6, 'score_7': 7, '9_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', 'october 29_6': 'october 29', 'score_7': 'score', '9_8': '9'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'october 29_6': [0], 'score_7': [1], '9_8': [2]}

['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']

[['october 3', 'dallas', '3 - 4', 'colorado', 'turco', '17487', '0 - 1 - 0'], ['october 5', 'boston', '1 - 4', 'dallas', 'turco', '18532', '1 - 1 - 0'], ['october 6', 'dallas', '1 - 5', 'nashville', 'smith', '13079', '1 - 2 - 0'], ['october 10', 'los angeles', '1 - 5', 'dallas', 'turco', '16129', '2 - 2 - 0'], ['october 12', 'calgary', '3 - 2', 'dallas', 'turco', '17132', '2 - 2 - 1'], ['october 13', 'dallas', '1 - 2', 'chicago', 'stephan', '11868', '2 - 2 - 2'], ['october 17', 'dallas', '3 - 2', 'columbus', 'smith', '11820', '3 - 2 - 2'], ['october 20', 'anaheim', '1 - 3', 'dallas', 'turco', '18057', '4 - 2 - 2'], ['october 25', 'dallas', '1 - 2', 'los angeles', 'turco', '14559', '4 - 3 - 2'], ['october 27', 'dallas', '5 - 3', 'phoenix', 'smith', '13741', '5 - 3 - 2'], ['october 29', 'san jose', '4 - 2', 'dallas', 'turco', '17546', '5 - 4 - 2'], ['october 31', 'chicago', '5 - 4', 'dallas', 'smith', '14756', '5 - 5 - 2']]

1992 australian touring car season

https://en.wikipedia.org/wiki/1992_Australian_Touring_Car_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17902470-1.html.csv

unique

atcc round 5 was the only series that was won by tony longhurst in the 1992 australian touring car season .

{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'tony longhurst', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'tony longhurst'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to tony longhurst .', 'tostr': 'filter_eq { all_rows ; winner ; tony longhurst }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winner ; tony longhurst } }', 'tointer': 'select the rows whose winner record fuzzily matches to tony longhurst . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'tony longhurst'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to tony longhurst .', 'tostr': 'filter_eq { all_rows ; winner ; tony longhurst }'}, 'series'], 'result': 'atcc round 5', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner ; tony longhurst } ; series }'}, 'atcc round 5'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; winner ; tony longhurst } ; series } ; atcc round 5 }', 'tointer': 'the series record of this unqiue row is atcc round 5 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; winner ; tony longhurst } } ; eq { hop { filter_eq { all_rows ; winner ; tony longhurst } ; series } ; atcc round 5 } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to tony longhurst . there is only one such row in the table . the series record of this unqiue row is atcc round 5 .'}

and { only { filter_eq { all_rows ; winner ; tony longhurst } } ; eq { hop { filter_eq { all_rows ; winner ; tony longhurst } ; series } ; atcc round 5 } } = true

select the rows whose winner record fuzzily matches to tony longhurst . there is only one such row in the table . the series record of this unqiue row is atcc round 5 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winner_7': 7, 'tony longhurst_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'series_9': 9, 'atcc round 5_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winner_7': 'winner', 'tony longhurst_8': 'tony longhurst', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'series_9': 'series', 'atcc round 5_10': 'atcc round 5'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winner_7': [0], 'tony longhurst_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'series_9': [2], 'atcc round 5_10': [3]}

['date', 'series', 'circuit', 'city / state', 'winner', 'team']

[['23 feb', 'atcc round 1', 'amaroo park', 'sydney , new south wales', 'mark skaife', 'winfield team nissan'], ['8 mar', 'atcc round 2', 'sandown international raceway', 'melbourne , victoria', 'john bowe', 'shell ultra - high racing'], ['15 mar', 'atcc round 3', 'symmons plains raceway', 'launceston , tasmania', 'glenn seton', 'peter jackson racing'], ['5 apr', 'atcc round 4', 'winton motor raceway', 'benalla , victoria', 'mark skaife', 'winfield team nissan'], ['3 may', 'atcc round 5', 'lakeside international raceway', 'brisbane , queensland', 'tony longhurst', 'benson & hedges racing'], ['24 may', 'atcc round 6', 'eastern creek raceway', 'sydney , new south wales', 'john bowe', 'shell ultra - high racing'], ['31 may', 'atcc round 7', 'mallala motor sport park', 'mallala , south australia', 'mark skaife', 'winfield team nissan'], ['7 jun', 'atcc round 8', 'barbagallo raceway', 'perth , western australia', 'john bowe', 'shell ultra - high racing'], ['21 jun', 'atcc round 9', 'oran park raceway', 'sydney , new south wales', 'mark skaife', 'winfield team nissan'], ['13 sep', 'drink / drive sandown 500', 'sandown international raceway', 'melbourne , victoria', 'larry perkins steve harrington', 'bob jane t - marts racing'], ['4 oct', 'tooheys 1000', 'mount panorama circuit', 'bathurst , new south wales', 'mark skaife jim richards', 'winfield team nissan'], ['8 nov', 'clarke shoes group a finale', 'adelaide street circuit', 'adelaide , south australia', 'jim richards', 'winfield team nissan']]

1976 vfl season

https://en.wikipedia.org/wiki/1976_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10885968-7.html.csv

superlative

hawthorn had the highest away team score among all the games played in the 1976 vfl season .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'away team score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; away team score }'}, 'away team'], 'result': 'hawthorn', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; away team score } ; away team }'}, 'hawthorn'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; away team score } ; away team } ; hawthorn } = true', 'tointer': 'select the row whose away team score record of all rows is maximum . the away team record of this row is hawthorn .'}

eq { hop { argmax { all_rows ; away team score } ; away team } ; hawthorn } = true

select the row whose away team score record of all rows is maximum . the away team record of this row is hawthorn .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'away team score_5': 5, 'away team_6': 6, 'hawthorn_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'away team score_5': 'away team score', 'away team_6': 'away team', 'hawthorn_7': 'hawthorn'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'away team score_5': [0], 'away team_6': [1], 'hawthorn_7': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['fitzroy', '11.22 ( 88 )', 'south melbourne', '12.15 ( 87 )', 'junction oval', '11267', '15 may 1976'], ['carlton', '21.14 ( 140 )', 'richmond', '9.15 ( 69 )', 'princes park', '30095', '15 may 1976'], ['melbourne', '14.13 ( 97 )', 'hawthorn', '21.19 ( 145 )', 'mcg', '25876', '15 may 1976'], ['geelong', '13.17 ( 95 )', 'footscray', '14.9 ( 93 )', 'kardinia park', '30395', '15 may 1976'], ['st kilda', '9.15 ( 69 )', 'essendon', '14.13 ( 97 )', 'moorabbin oval', '19864', '15 may 1976'], ['collingwood', '11.9 ( 75 )', 'north melbourne', '5.16 ( 46 )', 'vfl park', '34051', '15 may 1976']]

gb railfreight

https://en.wikipedia.org/wiki/GB_Railfreight

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12085438-1.html.csv

ordinal

class 08 is the earliest introduced class in shunter type in the gb railfreight .

{'scope': 'subset', 'row': '1', 'col': '3', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'shunter'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'shunter'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; type ; shunter }', 'tointer': 'select the rows whose type record fuzzily matches to shunter .'}, 'introduced', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; type ; shunter } ; introduced ; 1 }'}, 'class'], 'result': 'class 08', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; type ; shunter } ; introduced ; 1 } ; class }'}, 'class 08'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; type ; shunter } ; introduced ; 1 } ; class } ; class 08 } = true', 'tointer': 'select the rows whose type record fuzzily matches to shunter . select the row whose introduced record of these rows is 1st minimum . the class record of this row is class 08 .'}

eq { hop { nth_argmin { filter_eq { all_rows ; type ; shunter } ; introduced ; 1 } ; class } ; class 08 } = true

select the rows whose type record fuzzily matches to shunter . select the row whose introduced record of these rows is 1st minimum . the class record of this row is class 08 .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'type_6': 6, 'shunter_7': 7, 'introduced_8': 8, '1_9': 9, 'class_10': 10, 'class 08_11': 11}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'type_6': 'type', 'shunter_7': 'shunter', 'introduced_8': 'introduced', '1_9': '1', 'class_10': 'class', 'class 08_11': 'class 08'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'type_6': [0], 'shunter_7': [0], 'introduced_8': [1], '1_9': [1], 'class_10': [2], 'class 08_11': [3]}

['class', 'type', 'introduced', 'fleet size', 'numbers']

[['class 08', 'shunter', '1953', '2', '08925 , 08934'], ['class 09', 'shunter', '1959', '2', '09002 , 09009'], ['class 20', 'diesel locomotive', '1957 - 1968', '9', '20096 , 107 , 142 , 189 , 227 311 , 314 , 901 , 905'], ['class 66', 'diesel locomotive', '2002', '48', '66701 - 733 , 735 - 751'], ['class 73', 'electro - diesel locomotive', '1966', '10', '73119 , 141 , 204 - 209 , 212 - 213'], ['class 92', 'electric locomotive', '1993', '7', '92020 , 021 , 032 , 040 , 044 - 046'], ['vanguard 0 - 6 - 0dh', 'diesel locomotive', '2011', '2', 'dh50 - 1 , dh50 - 2']]

thiago alves ( tennis )

https://en.wikipedia.org/wiki/Thiago_Alves_%28tennis%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14924949-12.html.csv

count

three of thiago alves tennis tournaments were held in brazil .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'brazil', 'result': '3', 'col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'brazil'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to brazil .', 'tostr': 'filter_eq { all_rows ; tournament ; brazil }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; tournament ; brazil } }', 'tointer': 'select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; tournament ; brazil } } ; 3 } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; tournament ; brazil } } ; 3 } = true

select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'tournament_5': 5, 'brazil_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'tournament_5': 'tournament', 'brazil_6': 'brazil', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'tournament_5': [0], 'brazil_6': [0], '3_7': [2]}

['date', 'tournament', 'surface', 'partnering', 'opponents', 'score']

[['april 10 , 2006', 'florianópolis , brazil', 'clay', 'júlio silva', 'máximo gonzález sergio roitman', '6 - 2 , 3 - 6 ,'], ['march 5 , 2007', 'salinas , ecuador', 'hard', 'franco ferreiro', 'scott lipsky david martin', '7 - 5 , 7 - 6 ( 11 - 9 )'], ['september 29 , 2008', 'aracaju , brazil', 'clay', 'joão souza', 'juan martín aranguren franco ferreiro', '6 - 4 , 6 - 4'], ['november 3 , 2008', 'guayaquil , ecuador', 'clay', 'ricardo hocevar', 'sebastián decoud santiago giraldo', '6 - 4 , 6 - 4'], ['may 12 , 2012', 'rio quente , brazil', 'hard', 'augusto laranja', 'guido andreozzi marcel felder', '6 - 3 , 6 - 3']]

1983 - 84 coupe de france

https://en.wikipedia.org/wiki/1983%E2%80%9384_Coupe_de_France

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17786294-1.html.csv

superlative

as monaco had the highest score , six , between both rounds during the 1983-1984 coupe de france .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; score }'}, 'team 1'], 'result': 'as monaco ( d1 )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; team 1 }'}, 'as monaco ( d1 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; team 1 } ; as monaco ( d1 ) } = true', 'tointer': 'select the row whose score record of all rows is maximum . the team 1 record of this row is as monaco ( d1 ) .'}

eq { hop { argmax { all_rows ; score } ; team 1 } ; as monaco ( d1 ) } = true

select the row whose score record of all rows is maximum . the team 1 record of this row is as monaco ( d1 ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'score_5': 5, 'team 1_6': 6, 'as monaco (d1)_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'score_5': 'score', 'team 1_6': 'team 1', 'as monaco (d1)_7': 'as monaco ( d1 )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'score_5': [0], 'team 1_6': [1], 'as monaco (d1)_7': [2]}

['team 1', 'score', 'team 2', '1st round', '2nd round']

[['rc lens ( d1 )', '1 - 0', 'rc strasbourg ( d1 )', '1 - 0', '0 - 0'], ['fc rouen ( d1 )', '2 - 4', 'stade lavallois ( d1 )', '1 - 0', '1 - 3'], ['as monaco ( d1 )', '6 - 1', 'as nancy ( d1 )', '2 - 0', '4 - 1'], ['girondins de bordeaux ( d1 )', '2 - 3', 'fc mulhouse ( d2 )', '0 - 1', '2 - 2'], ['fc nantes ( d1 )', '4 - 4', 'olympique lyonnais ( d2 )', '0 - 0', '4 - 4'], ['as cannes ( d2 )', '4 - 1', 'fc sochaux - montbéliard ( d1 )', '3 - 0', '1 - 1'], ['sporting toulon var ( d1 )', '2 - 1', 'en avant guingamp ( d2 )', '2 - 0', '0 - 1'], ['fc metz ( d1 )', '5 - 1', 'besançon rc ( d2 )', '4 - 0', '1 - 1']]

united states house of representatives elections , 2006

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-43.html.csv

majority

most of tennessee 's representatives were first elected prior to the year 2000 .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '2000', 'subset': None}

{'func': 'most_less', 'args': ['all_rows', 'first elected', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the first elected records of all rows , most of them are less than 2000 .', 'tostr': 'most_less { all_rows ; first elected ; 2000 } = true'}

most_less { all_rows ; first elected ; 2000 } = true

for the first elected records of all rows , most of them are less than 2000 .

1

1

{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'first elected_3': 3, '2000_4': 4}

{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'first elected_3': 'first elected', '2000_4': '2000'}

{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'first elected_3': [0], '2000_4': [0]}

['district', 'incumbent', 'party', 'first elected', 'results']

[['tennessee 1', 'william l jenkins', 'republican', '1996', 'retired republican hold'], ['tennessee 2', 'jimmy duncan jr', 'republican', '1998', 're - elected'], ['tennessee 3', 'zach wamp', 'republican', '1994', 're - elected'], ['tennessee 4', 'lincoln davis', 'democratic', '2002', 're - elected'], ['tennessee 5', 'jim cooper', 'democratic', '2002', 're - elected'], ['tennessee 6', 'bart gordon', 'democratic', '1984', 're - elected'], ['tennessee 7', 'marsha blackburn', 'republican', '2002', 're - elected'], ['tennessee 8', 'john tanner', 'democratic', '1988', 're - elected'], ['tennessee 9', 'harold ford jr', 'democratic', '1996', 'retired to run for us senate democratic hold']]

1930 vfl season

https://en.wikipedia.org/wiki/1930_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767641-4.html.csv

comparative

north melbourne recorded a higher score than south melbourne .

{'row_1': '1', 'row_2': '5', 'col': '4', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'north melbourne'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne .', 'tostr': 'filter_eq { all_rows ; away team ; north melbourne }'}, 'away team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'south melbourne'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne .', 'tostr': 'filter_eq { all_rows ; away team ; south melbourne }'}, 'away team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } } = true', 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } } = true

select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'away team_7': 7, 'north melbourne_8': 8, 'away team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'away team_11': 11, 'south melbourne_12': 12, 'away team score_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'away team_7': 'away team', 'north melbourne_8': 'north melbourne', 'away team score_9': 'away team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'away team_11': 'away team', 'south melbourne_12': 'south melbourne', 'away team score_13': 'away team score'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'away team_7': [0], 'north melbourne_8': [0], 'away team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'away team_11': [1], 'south melbourne_12': [1], 'away team score_13': [3]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['melbourne', '18.15 ( 123 )', 'north melbourne', '15.12 ( 102 )', 'mcg', '8662', '24 may 1930'], ['footscray', '9.10 ( 64 )', 'richmond', '14.7 ( 91 )', 'western oval', '20000', '24 may 1930'], ['essendon', '14.12 ( 96 )', 'hawthorn', '8.13 ( 61 )', 'windy hill', '15000', '24 may 1930'], ['collingwood', '10.12 ( 72 )', 'geelong', '12.18 ( 90 )', 'victoria park', '17000', '24 may 1930'], ['carlton', '20.18 ( 138 )', 'south melbourne', '11.18 ( 84 )', 'princes park', '21000', '24 may 1930'], ['st kilda', '15.18 ( 108 )', 'fitzroy', '8.10 ( 58 )', 'junction oval', '26000', '24 may 1930']]

locomotives of the great western railway

https://en.wikipedia.org/wiki/Locomotives_of_the_Great_Western_Railway

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1169521-14.html.csv

superlative

the 0 - 6 - 2t by robert stephenson & co has the highest quantity of locomotives in the great western railway .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'quantity'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; quantity }'}, 'manufacturer'], 'result': 'robert stephenson & co', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; quantity } ; manufacturer }'}, 'robert stephenson & co'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; robert stephenson & co } = true', 'tointer': 'select the row whose quantity record of all rows is maximum . the manufacturer record of this row is robert stephenson & co .'}

eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; robert stephenson & co } = true

select the row whose quantity record of all rows is maximum . the manufacturer record of this row is robert stephenson & co .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'quantity_5': 5, 'manufacturer_6': 6, 'robert stephenson & co_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'quantity_5': 'quantity', 'manufacturer_6': 'manufacturer', 'robert stephenson & co_7': 'robert stephenson & co'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'quantity_5': [0], 'manufacturer_6': [1], 'robert stephenson & co_7': [2]}

['manufacturer', 'type', 'quantity', 'ptrd nos', 'gwr nos']

[['robert stephenson & co', '0 - 6 - 2t', '7', '8 - 14', '183 - 187'], ['hudswell clarke', '0 - 6 - 0st', '6', '22 - 27', '808 - 809 , 811 - 814'], ['robert stephenson & co', '0 - 6 - 0st', '2', '3 , 15', '815 , 816'], ['sharp , stewart & co', '2 - 4 - 0t', '1', '37', '1189'], ['sharp , stewart & co', '2 - 4 - 2t', '1', '36', '1326'], ['sharp , stewart & co', '0 - 8 - 2t', '3', '17 - 19', '1358 - 1360'], ['cooke locomotive & machine works', '0 - 8 - 2t', '2', '20 - 21', '1378 - 1379']]

new zealand general election , 1931

https://en.wikipedia.org/wiki/New_Zealand_general_election%2C_1931

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1189910-1.html.csv

ordinal

during the new zealand general election , country party leader harold rushworth recieved the fewest votes .

{'scope': 'all', 'row': '5', 'col': '3', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'votes', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; votes ; 1 }'}, 'party'], 'result': 'country party', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; votes ; 1 } ; party }'}, 'country party'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; votes ; 1 } ; party } ; country party } = true', 'tointer': 'select the row whose votes record of all rows is 1st minimum . the party record of this row is country party .'}

eq { hop { nth_argmin { all_rows ; votes ; 1 } ; party } ; country party } = true

select the row whose votes record of all rows is 1st minimum . the party record of this row is country party .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'votes_5': 5, '1_6': 6, 'party_7': 7, 'country party_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'votes_5': 'votes', '1_6': '1', 'party_7': 'party', 'country party_8': 'country party'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'votes_5': [0], '1_6': [0], 'party_7': [1], 'country party_8': [2]}

['party', 'leader', 'votes', 'percentage', 'seats']

[['reform', 'gordon coates', '190170', '54.03', '28'], ['united', 'george forbes', '120801', '54.03', '19'], ['28 independents ( in support of coalition )', '28 independents ( in support of coalition )', '75069', '54.03', '4'], ['labour', 'harry holland', '244867', '34.27', '24'], ['country party', 'harold rushworth', '16710', '2.34', '1'], ['41 independents ( including harry atmore )', '41 independents ( including harry atmore )', '66894', '9.36', '4'], ['coalition win', 'total votes', '714511', '100 %', '80']]

history of the midlands merit league

https://en.wikipedia.org/wiki/History_of_the_Midlands_Merit_League

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21991074-3.html.csv

ordinal

of the midlands merit league teams that played 10 or more matches , south humber rabbitohs had the third highest points for .

{'scope': 'subset', 'row': '6', 'col': '8', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'greater_than_eq', 'value': '10'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'played', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; played ; 10 }', 'tointer': 'select the rows whose played record is greater than or equal to 10 .'}, 'pts for', '3'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_greater_eq { all_rows ; played ; 10 } ; pts for ; 3 }'}, 'club'], 'result': 'south humber rabbitohs', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_greater_eq { all_rows ; played ; 10 } ; pts for ; 3 } ; club }'}, 'south humber rabbitohs'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_greater_eq { all_rows ; played ; 10 } ; pts for ; 3 } ; club } ; south humber rabbitohs } = true', 'tointer': 'select the rows whose played record is greater than or equal to 10 . select the row whose pts for record of these rows is 3rd maximum . the club record of this row is south humber rabbitohs .'}

eq { hop { nth_argmax { filter_greater_eq { all_rows ; played ; 10 } ; pts for ; 3 } ; club } ; south humber rabbitohs } = true

select the rows whose played record is greater than or equal to 10 . select the row whose pts for record of these rows is 3rd maximum . the club record of this row is south humber rabbitohs .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_greater_eq_0': 0, 'all_rows_5': 5, 'played_6': 6, '10_7': 7, 'pts for_8': 8, '3_9': 9, 'club_10': 10, 'south humber rabbitohs_11': 11}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_5': 'all_rows', 'played_6': 'played', '10_7': '10', 'pts for_8': 'pts for', '3_9': '3', 'club_10': 'club', 'south humber rabbitohs_11': 'south humber rabbitohs'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_greater_eq_0': [1], 'all_rows_5': [0], 'played_6': [0], '10_7': [0], 'pts for_8': [1], '3_9': [1], 'club_10': [2], 'south humber rabbitohs_11': [3]}

['position', 'club', 'played', 'won', 'drawn', 'lost', 'def', 'pts for', 'pts agst', 'long', 'points', 'average']

[['1', "moorends - thorne marauders ' a '", '8', '7', '0', '1', '0', '350', '116', '1', '23', '2.88'], ['2', "nottingham outlaws ' a '", '8', '7', '0', '1', '0', '286', '178', '1', '23', '2.88'], ['3', 'wigan riversiders', '12', '10', '0', '2', '0', '542', '210', '2', '34', '2.83'], ['4', "sheffield forgers ' a '", '11', '9', '0', '2', '0', '432', '294', '0', '29', '2.64'], ['5', "birmingham bulldogs ' a '", '10', '6', '0', '4', '0', '306', '269', '1', '23', '2.30'], ['6', 'south humber rabbitohs', '11', '6', '0', '4', '1', '338', '324', '1', '23', '2.09'], ['7', 'german exiles', '6', '3', '0', '3', '0', '234', '152', '0', '12', '2.00'], ['8', 'crewe & nantwich steamers', '9', '4', '0', '4', '1', '230', '282', '1', '16', '1.77'], ['9', 'north derbyshire chargers', '11', '2', '0', '8', '1', '293', '410', '1', '15', '1.44'], ['10', 'chester gladiators', '5', '1', '0', '4', '0', '84', '204', '3', '10', '2.00'], ['11', 'wolverhampton warlords', '4', '1', '0', '3', '0', '80', '138', '1', '7', '1.75'], ['12', "telford raiders ' a '", '4', '1', '0', '3', '0', '94', '184', '1', '7', '1.75'], ['13', "east riding ' a '", '5', '2', '0', '2', '1', '132', '158', '0', '8', '1.60'], ['14', 'barton bulldogs', '3', '0', '0', '3', '0', '28', '148', '0', '3', '1.00'], ['15', "redditch ravens ' a '", '1', '0', '0', '1', '0', '16', '24', '0', '1', '1.00'], ['16', "scarborough pirates ' a '", '1', '0', '0', '1', '0', '8', '36', '0', '1', '1.00']]

jonas l.a

https://en.wikipedia.org/wiki/Jonas_L.A.

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12441518-1.html.csv

aggregation

the average number of episodes each character of jonas l.a appeared in is 33.2 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '33.2', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'of episodes'], 'result': '33.2', 'ind': 0, 'tostr': 'avg { all_rows ; of episodes }'}, '33.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; of episodes } ; 33.2 } = true', 'tointer': 'the average of the of episodes record of all rows is 33.2 .'}

round_eq { avg { all_rows ; of episodes } ; 33.2 } = true

the average of the of episodes record of all rows is 33.2 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'of episodes_4': 4, '33.2_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'of episodes_4': 'of episodes', '33.2_5': '33.2'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'of episodes_4': [0], '33.2_5': [1]}

['character', 'portrayed by', 'main cast seasons', 'recurring cast seasons', 'of episodes']

[['nick lucas', 'nick jonas', 'seasons 1 - 2', 'appears in all seasons', '34'], ['joe lucas', 'joe jonas', 'seasons 1 - 2', 'appears in all seasons', '34'], ['kevin lucas', 'kevin jonas', 'seasons 1 - 2', 'appears in all seasons', '34'], ['stella malone', 'chelsea kane', 'seasons 1 - 2', 'appears in all seasons', '34'], ['macy misa', 'nicole anderson', 'seasons 1 - 2', 'appears in all seasons', '30']]

1986 - 87 segunda división

https://en.wikipedia.org/wiki/1986%E2%80%9387_Segunda_Divisi%C3%B3n

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12109851-6.html.csv

count

five of the teams in the 1986-87 segunda division had a negative goal difference .

{'scope': 'all', 'criterion': 'less_than', 'value': '0', 'result': '5', 'col': '10', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'goal difference', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goal difference record is less than 0 .', 'tostr': 'filter_less { all_rows ; goal difference ; 0 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; goal difference ; 0 } }', 'tointer': 'select the rows whose goal difference record is less than 0 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 5 } = true', 'tointer': 'select the rows whose goal difference record is less than 0 . the number of such rows is 5 .'}

eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 5 } = true

select the rows whose goal difference record is less than 0 . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'goal difference_5': 5, '0_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'goal difference_5': 'goal difference', '0_6': '0', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'goal difference_5': [0], '0_6': [0], '5_7': [2]}

['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference']

[['1', 'barcelona atlã ¨ tic', '44', '42 - 2', '16', '10', '18', '56', '58', '- 2'], ['2', 'ue figueres', '44', '42 - 2', '15', '12', '17', '59', '53', '+ 6'], ['3', 'cartagena fc', '44', '42 - 2', '14', '14', '16', '52', '67', '- 15'], ['4', 'real oviedo', '44', '40 - 4', '13', '14', '17', '50', '64', '- 14'], ['5', 'castilla cf', '44', '33 - 11', '11', '11', '22', '49', '71', '- 22'], ['6', 'jerez deportivo', '44', '22 - 22', '5', '12', '27', '32', '78', '- 46']]

2007 steelback grand prix

https://en.wikipedia.org/wiki/2007_Steelback_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12126919-2.html.csv

count

there were 2 drivers with a +1 lap completion time during the 2007 steelback grand prix .

{'scope': 'all', 'criterion': 'equal', 'value': '+1 lap', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time / retired', '+1 lap'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time / retired record fuzzily matches to +1 lap .', 'tostr': 'filter_eq { all_rows ; time / retired ; +1 lap }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; time / retired ; +1 lap } }', 'tointer': 'select the rows whose time / retired record fuzzily matches to +1 lap . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; time / retired ; +1 lap } } ; 2 } = true', 'tointer': 'select the rows whose time / retired record fuzzily matches to +1 lap . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; time / retired ; +1 lap } } ; 2 } = true

select the rows whose time / retired record fuzzily matches to +1 lap . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'time / retired_5': 5, '+1 lap_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'time / retired_5': 'time / retired', '+1 lap_6': '+1 lap', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'time / retired_5': [0], '+1 lap_6': [0], '2_7': [2]}

['driver', 'team', 'laps', 'time / retired', 'grid', 'points']

[['will power', 'team australia', '73', '1:45:58.568', '7', '31'], ['neel jani', 'pkv racing', '73', '+ 2.972', '9', '28'], ['justin wilson', 'rsports', '73', '+ 3.480', '2', '25'], ['simon pagenaud', 'team australia', '73', '+ 5.643', '4', '23'], ['bruno junqueira', 'dale coyne racing', '73', '+ 20.738', '5', '21'], ['robert doornbos', 'minardi team usa', '72', '+ 1 lap', '12', '19'], ['ryan dalziel', 'pacific coast motorsports', '72', '+ 1 lap', '11', '17'], ['alex tagliani', 'rsports', '71', '+ 2 laps', '6', '15'], ['sãbastien bourdais', 'n / h / l racing', '67', 'contact', '1', '16'], ['oriol servia', 'forsythe racing', '56', 'contact', '3', '11'], ['graham rahal', 'n / h / l racing', '52', 'contact', '15', '10'], ['dan clarke', 'minardi team usa', '43', 'contact', '13', '9'], ['jan heylen', 'conquest racing', '1', 'contact', '8', '8'], ['paul tracy', 'forsythe racing', '0', 'contact', '10', '7'], ['tristan gommendy', 'pkv racing', '0', 'contact', '14', '6'], ['katherine legge', 'dale coyne racing', '0', 'contact', '16', '5'], ['alex figge', 'pacific coast motorsports', '0', 'contact', '17', '4']]

gilmour racing

https://en.wikipedia.org/wiki/Gilmour_Racing

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16351380-1.html.csv

majority

most of the races were the australian formula 3 championship .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'formula 3 championship', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'series', 'formula 3 championship'], 'result': True, 'ind': 0, 'tointer': 'for the series records of all rows , most of them fuzzily match to formula 3 championship .', 'tostr': 'most_eq { all_rows ; series ; formula 3 championship } = true'}

most_eq { all_rows ; series ; formula 3 championship } = true

for the series records of all rows , most of them fuzzily match to formula 3 championship .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'series_3': 3, 'formula 3 championship_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'series_3': 'series', 'formula 3 championship_4': 'formula 3 championship'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'series_3': [0], 'formula 3 championship_4': [0]}

['season', 'series', 'points', 'position', 'driver']

[['2001', 'queensland formula ford championship', '216', '2nd', 'chris gilmour'], ['2002', 'queensland formula ford championship', '234', '2nd', 'chris gilmour'], ['2003', 'queensland formula ford championship', '222', '1st', 'chris gilmour'], ['2004', 'australian formula 3 championship', '235', '2nd', 'chris gilmour'], ['2005', 'australian formula 3 championship', '142', '4th', 'chris gilmour'], ['2006', 'australian formula 3 championship', '150', '4th', 'chris gilmour'], ['2007', 'australian formula 3 championship', '52', '8th', 'chris gilmour'], ['2008', 'australian formula 3 championship - national class', '228', '1st', 'chris gilmour'], ['2009', 'australian formula 3 championship - national class', '93', '4th', 'chris gilmour'], ['2010', 'australian formula 3 championship', '90', '5th', 'chris gilmour'], ['2011', 'australian formula 3 championship', '210', '1st', 'chris gilmour']]

tolapai

https://en.wikipedia.org/wiki/Tolapai

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14114066-1.html.csv

comparative

the slj6b sspec tolapai processor had a higher release price than the slj6c sspec processor .

{'row_1': '4', 'row_2': '3', 'col': '9', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sspec number', 'slj6b ( b1 )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sspec number record fuzzily matches to slj6b ( b1 ) .', 'tostr': 'filter_eq { all_rows ; sspec number ; slj6b ( b1 ) }'}, 'release price ( usd )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; sspec number ; slj6b ( b1 ) } ; release price ( usd ) }', 'tointer': 'select the rows whose sspec number record fuzzily matches to slj6b ( b1 ) . take the release price ( usd ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sspec number', 'slj6c ( b1 )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose sspec number record fuzzily matches to slj6c ( b1 ) .', 'tostr': 'filter_eq { all_rows ; sspec number ; slj6c ( b1 ) }'}, 'release price ( usd )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; sspec number ; slj6c ( b1 ) } ; release price ( usd ) }', 'tointer': 'select the rows whose sspec number record fuzzily matches to slj6c ( b1 ) . take the release price ( usd ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; sspec number ; slj6b ( b1 ) } ; release price ( usd ) } ; hop { filter_eq { all_rows ; sspec number ; slj6c ( b1 ) } ; release price ( usd ) } } = true', 'tointer': 'select the rows whose sspec number record fuzzily matches to slj6b ( b1 ) . take the release price ( usd ) record of this row . select the rows whose sspec number record fuzzily matches to slj6c ( b1 ) . take the release price ( usd ) record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; sspec number ; slj6b ( b1 ) } ; release price ( usd ) } ; hop { filter_eq { all_rows ; sspec number ; slj6c ( b1 ) } ; release price ( usd ) } } = true

select the rows whose sspec number record fuzzily matches to slj6b ( b1 ) . take the release price ( usd ) record of this row . select the rows whose sspec number record fuzzily matches to slj6c ( b1 ) . take the release price ( usd ) record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'sspec number_7': 7, 'slj6b (b1)_8': 8, 'release price ( usd )_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'sspec number_11': 11, 'slj6c (b1)_12': 12, 'release price ( usd )_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'sspec number_7': 'sspec number', 'slj6b (b1)_8': 'slj6b ( b1 )', 'release price ( usd )_9': 'release price ( usd )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'sspec number_11': 'sspec number', 'slj6c (b1)_12': 'slj6c ( b1 )', 'release price ( usd )_13': 'release price ( usd )'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'sspec number_7': [0], 'slj6b (b1)_8': [0], 'release price ( usd )_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'sspec number_11': [1], 'slj6c (b1)_12': [1], 'release price ( usd )_13': [3]}

['sspec number', 'frequency', 'l2 cache', 'mult', 'voltage', 'socket', 'release date', 'part number ( s )', 'release price ( usd )']

[['without quickassist', 'without quickassist', 'without quickassist', 'without quickassist', 'without quickassist', 'without quickassist', 'without quickassist', 'without quickassist', 'without quickassist'], ['slj6d ( b1 ) slj6f ( b1 )', '600 mhz', '256 kb', '6', '1v', 'fcbga1088', '2008 q3', 'nu80579ez600cnu80579ez600ct', '40 - 51'], ['slj6c ( b1 )', '1.07 ghz', '256 kb', '8', '1.3 v', 'fcbga1088', '2008 q3', 'nu80579ez004c', '42'], ['slj6b ( b1 )', '1.2 ghz', '256 kb', '9', '1.3 v', 'fcbga1088', '2008 q3', 'nu80579ez009c', '70'], ['with quickassist', 'with quickassist', 'with quickassist', 'with quickassist', 'with quickassist', 'with quickassist', 'with quickassist', 'with quickassist', 'with quickassist'], ['slj6a ( b1 )', '600 mhz', '256 kb', '6', '1v', 'fcbga1088', '2008 q3', 'nu80579eb600c', '44'], ['slj69 ( b1 ) slj6e ( b1 )', '1.07 ghz', '256 kb', '8', '1.3 v', 'fcbga1088', '2008 q3', 'nu80579ed004cnu80579ed004ct', '66 - 102'], ['slj68 ( b1 )', '1.2 ghz', '256 kb', '9', '1.3 v', 'fcbga1088', '2008 q3', 'nu80579ed009c', '102']]

politics of friuli - venezia giulia

https://en.wikipedia.org/wiki/Politics_of_Friuli-Venezia_Giulia

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18568694-2.html.csv

aggregation

the major municipalities of friuli-venezia giulia have a total population of 521,203 .

{'scope': 'all', 'col': '2', 'type': 'sum', 'result': '521,203', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'inhabitants'], 'result': '521,203', 'ind': 0, 'tostr': 'sum { all_rows ; inhabitants }'}, '521,203'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; inhabitants } ; 521,203 } = true', 'tointer': 'the sum of the inhabitants record of all rows is 521,203 .'}

round_eq { sum { all_rows ; inhabitants } ; 521,203 } = true

the sum of the inhabitants record of all rows is 521,203 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'inhabitants_4': 4, '521,203_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'inhabitants_4': 'inhabitants', '521,203_5': '521,203'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'inhabitants_4': [0], '521,203_5': [1]}

['municipality', 'inhabitants', 'mayor', 'party', 'election']

[['trieste', '205535', 'roberto cosolini', 'democratic party', '2011'], ['udine', '99627', 'furio honsell', 'democratic party', '2008'], ['pordenone', '51723', 'claudio pedrotti', 'democratic party', '2011'], ['gorizia', '35798', 'ettore romoli', 'the people of freedom', '2012'], ['monfalcone', '27877', 'silvia altran', 'democratic party', '2011'], ['sacile', '20227', 'roberto ceraolo', 'the people of freedom', '2009'], ['cordenons', '18470', 'mario ongaro', 'lega friuli - vg', '2011'], ['codroipo', '15887', 'fabio marchetti', 'the people of freedom', '2011'], ['azzano decimo', '15601', 'marco putto', 'democratic party', '2012'], ['porcia', '15443', 'stefano turchet', 'lega friuli - vg', '2009'], ['san vito al tagliamento', '15015', 'antonio di bisceglie', 'democratic party', '2011']]

shaun micheel

https://en.wikipedia.org/wiki/Shaun_Micheel

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1551537-3.html.csv

unique

the pga championship was the only tournament that shaun micheel ever won .

{'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'not_equal', 'value': '0', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is not equal to 0 .', 'tostr': 'filter_not_eq { all_rows ; wins ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; wins ; 0 } }', 'tointer': 'select the rows whose wins record is not equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is not equal to 0 .', 'tostr': 'filter_not_eq { all_rows ; wins ; 0 }'}, 'tournament'], 'result': 'pga championship', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; wins ; 0 } ; tournament }'}, 'pga championship'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; wins ; 0 } ; tournament } ; pga championship }', 'tointer': 'the tournament record of this unqiue row is pga championship .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_not_eq { all_rows ; wins ; 0 } ; tournament } ; pga championship } } = true', 'tointer': 'select the rows whose wins record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .'}

and { only { filter_not_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_not_eq { all_rows ; wins ; 0 } ; tournament } ; pga championship } } = true

select the rows whose wins record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_not_eq_0': 0, 'all_rows_6': 6, 'wins_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'pga championship_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_not_eq_0': 'filter_not_eq', 'all_rows_6': 'all_rows', 'wins_7': 'wins', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'pga championship_10': 'pga championship'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_not_eq_0': [1, 2], 'all_rows_6': [0], 'wins_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'pga championship_10': [3]}

['tournament', 'wins', 'top - 5', 'top - 25', 'events', 'cuts made']

[['masters tournament', '0', '0', '1', '5', '1'], ['us open', '0', '0', '1', '7', '3'], ['the open championship', '0', '0', '0', '4', '2'], ['pga championship', '1', '2', '3', '10', '6'], ['totals', '1', '2', '5', '26', '12']]

2009 - 10 miami heat season

https://en.wikipedia.org/wiki/2009%E2%80%9310_Miami_Heat_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23248967-10.html.csv

count

in the 2009 - 10 miami heat season , among the games where udonis haslem had the most high rebounds , three of them had a dwyane wade as a high assister .

{'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'dwyane wade', 'result': '3', 'col': '7', 'subset': {'col': '6', 'criterion': 'fuzzily_match', 'value': 'udonis haslem'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'udonis haslem'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high rebounds ; udonis haslem }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to udonis haslem .'}, 'high assists', 'dwyane wade'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade .', 'tostr': 'filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } } ; 3 } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .'}

eq { count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } } ; 3 } = true

select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'high rebounds_6': 6, 'udonis haslem_7': 7, 'high assists_8': 8, 'dwyane wade_9': 9, '3_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'high rebounds_6': 'high rebounds', 'udonis haslem_7': 'udonis haslem', 'high assists_8': 'high assists', 'dwyane wade_9': 'dwyane wade', '3_10': '3'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'high rebounds_6': [0], 'udonis haslem_7': [0], 'high assists_8': [1], 'dwyane wade_9': [1], '3_10': [3]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['76', 'april 2', 'indiana', 'w 105 - 96 ( ot )', 'dwyane wade ( 43 )', 'udonis haslem ( 11 )', 'dwyane wade ( 6 )', 'conseco fieldhouse 16787', '42 - 34'], ['77', 'april 3', 'minnesota', 'w 97 - 84 ( ot )', 'dwyane wade ( 39 )', 'udonis haslem ( 17 )', 'carlos arroyo ( 9 )', 'target center 17601', '43 - 34'], ['78', 'april 7', 'philadelphia', 'w 99 - 95 ( ot )', 'dwyane wade ( 22 )', 'udonis haslem ( 11 )', 'mario chalmers ( 5 )', 'american airlines arena 18221', '44 - 34'], ['79', 'april 9', 'detroit', 'l 99 - 106 ( ot )', 'dwyane wade ( 19 )', 'udonis haslem ( 11 )', 'dwyane wade ( 9 )', 'american airlines arena 19600', '44 - 35'], ['80', 'april 11', 'new york', 'w 111 - 98 ( ot )', 'dwyane wade ( 32 )', 'udonis haslem ( 10 )', 'dwyane wade ( 5 )', 'madison square garden 19763', '45 - 35'], ['81', 'april 12', 'philadelphia', 'w 107 - 105 ( ot )', 'dwyane wade ( 30 )', 'quentin richardson ( 12 )', 'carlos arroyo ( 7 )', 'wachovia center 17401', '46 - 35']]

united states house of representatives elections , 1934

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1934

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342331-6.html.csv

unique

ralph r elise was the only republican who ran and lost to a democrat .

{'scope': 'all', 'row': '3', 'col': '5', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'lost re - election', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; lost re - election } }', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election }'}, 'incumbent'], 'result': 'ralph r eltse', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; lost re - election } ; incumbent }'}, 'ralph r eltse'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; lost re - election } ; incumbent } ; ralph r eltse }', 'tointer': 'the incumbent record of this unqiue row is ralph r eltse .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; lost re - election } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election } ; incumbent } ; ralph r eltse } } = true', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election . there is only one such row in the table . the incumbent record of this unqiue row is ralph r eltse .'}

and { only { filter_eq { all_rows ; result ; lost re - election } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election } ; incumbent } ; ralph r eltse } } = true

select the rows whose result record fuzzily matches to lost re - election . there is only one such row in the table . the incumbent record of this unqiue row is ralph r eltse .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'lost re - election_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'ralph r eltse_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'lost re - election_8': 'lost re - election', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'ralph r eltse_10': 'ralph r eltse'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'lost re - election_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'ralph r eltse_10': [3]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['california 2', 'harry lane englebright', 'republican', '1926', 're - elected', 'harry lane englebright ( r ) unopposed'], ['california 6', 'albert e carter', 'republican', '1924', 're - elected', 'albert e carter ( r ) unopposed'], ['california 7', 'ralph r eltse', 'republican', '1932', 'lost re - election democratic gain', 'john h tolan ( d ) 52.3 % ralph r eltse ( r ) 47.7 %'], ['california 8', 'john j mcgrath', 'democratic', '1932', 're - elected', 'john j mcgrath ( d ) unopposed'], ['california 9', 'denver s church', 'democratic', '1932', 'retired republican gain', 'bertrand w gearhart ( r ) unopposed'], ['california 16', 'john f dockweiler', 'democratic', '1932', 're - elected', 'john f dockweiler ( d ) unopposed'], ['california 19', 'sam l collins', 'republican', '1932', 're - elected', 'sam l collins ( r ) 88.8 % a b hillabold ( i ) 11.2 %']]

dwsn

https://en.wikipedia.org/wiki/DWSN

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17487395-1.html.csv

comparative

mom 's radio 88.3 cebu comsumes more power , in fact twice as much , than mom 's radio 101.5 tacloban .

{'row_1': '4', 'row_2': '5', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'branding', "mom 's radio 88.3 cebu"], 'result': None, 'ind': 0, 'tointer': "select the rows whose branding record fuzzily matches to mom 's radio 88.3 cebu .", 'tostr': "filter_eq { all_rows ; branding ; mom 's radio 88.3 cebu }"}, 'power ( kw )'], 'result': None, 'ind': 2, 'tostr': "hop { filter_eq { all_rows ; branding ; mom 's radio 88.3 cebu } ; power ( kw ) }", 'tointer': "select the rows whose branding record fuzzily matches to mom 's radio 88.3 cebu . take the power ( kw ) record of this row ."}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'branding', "mom 's radio 101.5 tacloban"], 'result': None, 'ind': 1, 'tointer': "select the rows whose branding record fuzzily matches to mom 's radio 101.5 tacloban .", 'tostr': "filter_eq { all_rows ; branding ; mom 's radio 101.5 tacloban }"}, 'power ( kw )'], 'result': None, 'ind': 3, 'tostr': "hop { filter_eq { all_rows ; branding ; mom 's radio 101.5 tacloban } ; power ( kw ) }", 'tointer': "select the rows whose branding record fuzzily matches to mom 's radio 101.5 tacloban . take the power ( kw ) record of this row ."}], 'result': True, 'ind': 4, 'tostr': "greater { hop { filter_eq { all_rows ; branding ; mom 's radio 88.3 cebu } ; power ( kw ) } ; hop { filter_eq { all_rows ; branding ; mom 's radio 101.5 tacloban } ; power ( kw ) } } = true", 'tointer': "select the rows whose branding record fuzzily matches to mom 's radio 88.3 cebu . take the power ( kw ) record of this row . select the rows whose branding record fuzzily matches to mom 's radio 101.5 tacloban . take the power ( kw ) record of this row . the first record is greater than the second record ."}

greater { hop { filter_eq { all_rows ; branding ; mom 's radio 88.3 cebu } ; power ( kw ) } ; hop { filter_eq { all_rows ; branding ; mom 's radio 101.5 tacloban } ; power ( kw ) } } = true

select the rows whose branding record fuzzily matches to mom 's radio 88.3 cebu . take the power ( kw ) record of this row . select the rows whose branding record fuzzily matches to mom 's radio 101.5 tacloban . take the power ( kw ) record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'branding_7': 7, "mom 's radio 88.3 cebu_8": 8, 'power (kw)_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'branding_11': 11, "mom 's radio 101.5 tacloban_12": 12, 'power (kw)_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'branding_7': 'branding', "mom 's radio 88.3 cebu_8": "mom 's radio 88.3 cebu", 'power (kw)_9': 'power ( kw )', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'branding_11': 'branding', "mom 's radio 101.5 tacloban_12": "mom 's radio 101.5 tacloban", 'power (kw)_13': 'power ( kw )'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'branding_7': [0], "mom 's radio 88.3 cebu_8": [0], 'power (kw)_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'branding_11': [1], "mom 's radio 101.5 tacloban_12": [1], 'power (kw)_13': [3]}

['branding', 'callsign', 'frequency', 'power ( kw )', 'location']

[["mom 's radio 97.9 laoag", 'dwsn - fm', '97.9 mhz', '5 kw', 'laoag'], ["mom 's radio 95.9 naga", 'dzrb - fm', '95.9 mhz', '10 kw', 'naga'], ["mom 's radio 90.3 bacolod", 'dycp - fm', '90.3 mhz', '5 kw', 'bacolod'], ["mom 's radio 88.3 cebu", 'dyap - fm', '88.3 mhz', '5 kw', 'cebu'], ["mom 's radio 101.5 tacloban", 'dyjp - fm', '101.5 mhz', '2.5 kw', 'tacloban'], ["mom 's radio 101.9 zamboanga", 'dxjp - fm', '101.9 mhz', '5 kw', 'zamboanga'], ["mom 's radio 97.9 davao", 'dxss', '97.9 mhz', '10 kw', 'davao']]

greg sacks

https://en.wikipedia.org/wiki/Greg_Sacks

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2308381-1.html.csv

unique

in greg sacks ' career , 1987 was the only year that he was on the team dingman brothers racing .

{'scope': 'all', 'row': '4', 'col': '11', 'col_other': '1', 'criterion': 'equal', 'value': 'dingman brothers racing', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team ( s )', 'dingman brothers racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team ( s ) record fuzzily matches to dingman brothers racing .', 'tostr': 'filter_eq { all_rows ; team ( s ) ; dingman brothers racing }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } }', 'tointer': 'select the rows whose team ( s ) record fuzzily matches to dingman brothers racing . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team ( s )', 'dingman brothers racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team ( s ) record fuzzily matches to dingman brothers racing .', 'tostr': 'filter_eq { all_rows ; team ( s ) ; dingman brothers racing }'}, 'year'], 'result': '1987', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year }'}, '1987'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 }', 'tointer': 'the year record of this unqiue row is 1987 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } } ; eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 } } = true', 'tointer': 'select the rows whose team ( s ) record fuzzily matches to dingman brothers racing . there is only one such row in the table . the year record of this unqiue row is 1987 .'}

and { only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } } ; eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 } } = true

select the rows whose team ( s ) record fuzzily matches to dingman brothers racing . there is only one such row in the table . the year record of this unqiue row is 1987 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team (s)_7': 7, 'dingman brothers racing_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1987_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team (s)_7': 'team ( s )', 'dingman brothers racing_8': 'dingman brothers racing', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1987_10': '1987'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'team (s)_7': [0], 'dingman brothers racing_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1987_10': [3]}

['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )']

[['1983', '5', '0', '0', '0', '0', '25.6', '30.4', '8060', '47th', '5 sacks & sons'], ['1984', '29', '0', '0', '1', '0', '24.3', '25.1', '75183', '19th', '51 sacks & sons'], ['1986', '8', '0', '0', '1', '0', '22.4', '30.4', '64810', '41st', '10 digard motorsports'], ['1987', '16', '0', '0', '0', '0', '23.6', '29.8', '54815', '33rd', '50 dingman brothers racing'], ['1990', '16', '0', '2', '4', '1', '18.6', '20.8', '216148', '32nd', '17 / 18 / 46 hendrick motorsports'], ['1991', '11', '0', '0', '0', '0', '27.5', '30.4', '84215', '39th', '18 daytona speed inc 47 close racing'], ['1992', '20', '0', '0', '0', '0', '23.5', '25.1', '178120', '30th', '41 larry hedrick motorsports'], ['1993', '19', '0', '0', '1', '0', '24.3', '24.2', '168055', '35th', '9 melling racing 68 tristar motorsports'], ['1994', '31', '0', '0', '3', '1', '19.7', '27.0', '411728', '31st', '77 us motorsports inc'], ['1998', '7', '0', '0', '0', '0', '23.6', '35.3', '296880', '53rd', '98 yarborough - burdette motorsports'], ['2004', '3', '0', '0', '0', '0', '36.3', '41.7', '154100', '71st', '13 daytona speed inc']]

1963 syracuse orangemen football team

https://en.wikipedia.org/wiki/1963_Syracuse_Orangemen_football_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21091127-1.html.csv

majority

in games they won , the 1963 syracuse orangemen usually scored more than 20 points .

{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '20', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'win'}}

{'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'win'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; win }', 'tointer': 'select the rows whose result record fuzzily matches to win .'}, 'orangemen points', '20'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose result record fuzzily matches to win . for the orangemen points records of these rows , most of them are greater than 20 .', 'tostr': 'most_greater { filter_eq { all_rows ; result ; win } ; orangemen points ; 20 } = true'}

most_greater { filter_eq { all_rows ; result ; win } ; orangemen points ; 20 } = true

select the rows whose result record fuzzily matches to win . for the orangemen points records of these rows , most of them are greater than 20 .

2

2

{'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'result_4': 4, 'win_5': 5, 'orangemen points_6': 6, '20_7': 7}

{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'result_4': 'result', 'win_5': 'win', 'orangemen points_6': 'orangemen points', '20_7': '20'}

{'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'result_4': [0], 'win_5': [0], 'orangemen points_6': [1], '20_7': [1]}

['game', 'date', 'opponent', 'result', 'orangemen points', 'opponents', 'record']

[['1', 'sept 21', 'boston college', 'win', '32', '21', '1 - 0'], ['2', 'sept 28', 'kansas', 'loss', '0', '10', '1 - 1'], ['3', 'oct 5', 'holy cross', 'win', '48', '0', '2 - 1'], ['4', 'oct 11', 'ucla', 'win', '29', '7', '3 - 1'], ['5', 'oct 19', 'penn state', 'win', '19', '0', '4 - 1'], ['6', 'oct 26', 'oregon state', 'win', '31', '8', '5 - 1'], ['7', 'nov 2', 'pittsburgh', 'loss', '27', '35', '5 - 2'], ['8', 'nov 9', 'west virginia', 'win', '15', '13', '6 - 2'], ['9', 'nov 16', 'richmond', 'win', '50', '0', '7 - 2']]

list of actors who played president of the united states

https://en.wikipedia.org/wiki/List_of_actors_who_played_President_of_the_United_States

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1673723-9.html.csv

comparative

raymond massey was nominated for playing a president before anthony hopkins .

{'row_1': '1', 'row_2': '3', 'col': '1', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nominee', 'raymond massey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nominee record fuzzily matches to raymond massey .', 'tostr': 'filter_eq { all_rows ; nominee ; raymond massey }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nominee ; raymond massey } ; year }', 'tointer': 'select the rows whose nominee record fuzzily matches to raymond massey . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nominee', 'anthony hopkins'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nominee record fuzzily matches to anthony hopkins .', 'tostr': 'filter_eq { all_rows ; nominee ; anthony hopkins }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nominee ; anthony hopkins } ; year }', 'tointer': 'select the rows whose nominee record fuzzily matches to anthony hopkins . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; nominee ; raymond massey } ; year } ; hop { filter_eq { all_rows ; nominee ; anthony hopkins } ; year } } = true', 'tointer': 'select the rows whose nominee record fuzzily matches to raymond massey . take the year record of this row . select the rows whose nominee record fuzzily matches to anthony hopkins . take the year record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; nominee ; raymond massey } ; year } ; hop { filter_eq { all_rows ; nominee ; anthony hopkins } ; year } } = true

select the rows whose nominee record fuzzily matches to raymond massey . take the year record of this row . select the rows whose nominee record fuzzily matches to anthony hopkins . take the year record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nominee_7': 7, 'raymond massey_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nominee_11': 11, 'anthony hopkins_12': 12, 'year_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nominee_7': 'nominee', 'raymond massey_8': 'raymond massey', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nominee_11': 'nominee', 'anthony hopkins_12': 'anthony hopkins', 'year_13': 'year'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nominee_7': [0], 'raymond massey_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nominee_11': [1], 'anthony hopkins_12': [1], 'year_13': [3]}

['year', 'category', 'president', 'nominee', 'film', 'result']

[['1941', 'best actor', 'abraham lincoln', 'raymond massey', 'abe lincoln in illinois', 'nominated'], ['1976', 'best actor', 'harry s truman', 'james whitmore', "give 'em hell , harry !", 'nominated'], ['1996', 'best actor', 'richard nixon', 'anthony hopkins', 'nixon', 'nominated'], ['1998', 'best supporting actor', 'john quincy adams', 'anthony hopkins', 'amistad', 'nominated'], ['2009', 'best actor', 'richard nixon', 'frank langella', 'frost / nixon', 'nominated'], ['2013', 'best actor', 'abraham lincoln', 'daniel day - lewis', 'lincoln', 'won']]

ktlf

https://en.wikipedia.org/wiki/KTLF

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13831309-1.html.csv

comparative

ktaw has a lower frequency than the one ktml operates on .

{'row_1': '13', 'row_2': '12', 'col': '2', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'ktaw'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to ktaw .', 'tostr': 'filter_eq { all_rows ; call sign ; ktaw }'}, 'frequency mhz'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; ktaw } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to ktaw . take the frequency mhz record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'ktml'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to ktml .', 'tostr': 'filter_eq { all_rows ; call sign ; ktml }'}, 'frequency mhz'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; ktml } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to ktml . take the frequency mhz record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; call sign ; ktaw } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; ktml } ; frequency mhz } } = true', 'tointer': 'select the rows whose call sign record fuzzily matches to ktaw . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to ktml . take the frequency mhz record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; call sign ; ktaw } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; ktml } ; frequency mhz } } = true

select the rows whose call sign record fuzzily matches to ktaw . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to ktml . take the frequency mhz record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'call sign_7': 7, 'ktaw_8': 8, 'frequency mhz_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'call sign_11': 11, 'ktml_12': 12, 'frequency mhz_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'call sign_7': 'call sign', 'ktaw_8': 'ktaw', 'frequency mhz_9': 'frequency mhz', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'call sign_11': 'call sign', 'ktml_12': 'ktml', 'frequency mhz_13': 'frequency mhz'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'call sign_7': [0], 'ktaw_8': [0], 'frequency mhz_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'call sign_11': [1], 'ktml_12': [1], 'frequency mhz_13': [3]}

['call sign', 'frequency mhz', 'city of license', 'erp w', 'height m ( ft )', 'class', 'fcc info']

[['ktlc', '89.1', 'canon city , colorado', '1150', '-', 'c3', 'fcc'], ['ktcf', '89.5', 'dolores , colorado', '500', '-', 'a', 'fcc'], ['ktdu', '88.5', 'durango , colorado', '4000', '-', 'a', 'fcc'], ['ktmh', '89.9', 'montrose , colorado', '4000', '-', 'c1', 'fcc'], ['ktps', '89.7', 'pagosa springs , colorado', '200', '-', 'a', 'fcc'], ['ktsg', '91.7', 'steamboat springs , colorado', '2500', '-', 'c3', 'fcc'], ['ktol', '90.9', 'leadville , colorado', '450', '-', 'a', 'fcc'], ['ktpf', '91.3', 'salida , colorado', '390', '-', 'c2', 'fcc'], ['ktei', '90.7', 'placerville , colorado', '250', '-', 'a', 'fcc'], ['ktdl', '90.7', 'trinidad , colorado', '450', '-', 'a', 'fcc'], ['ktad', '89.9', 'sterling , colorado', '5000', '-', 'c3', 'fcc'], ['ktml', '91.5', 'south fork , colorado', '280', '-', 'c3', 'fcc'], ['ktaw', '89.3', 'walsenburg , colorado', '500', '-', 'a', 'fcc'], ['ktdx', '89.3', 'laramie , wyoming', '450', '-', 'a', 'fcc']]

2nd amateurliga bayern

https://en.wikipedia.org/wiki/2nd_Amateurliga_Bayern

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23224961-1.html.csv

comparative

bsc sending won oberbayern a before fc oberau won oberbayern a.

{'row_1': '3', 'row_2': '10', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'oberbayern a', 'bsc sendling'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose oberbayern a record fuzzily matches to bsc sendling .', 'tostr': 'filter_eq { all_rows ; oberbayern a ; bsc sendling }'}, 'season'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season }', 'tointer': 'select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'oberbayern a', 'fc oberau'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose oberbayern a record fuzzily matches to fc oberau .', 'tostr': 'filter_eq { all_rows ; oberbayern a ; fc oberau }'}, 'season'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season }', 'tointer': 'select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season } ; hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season } } = true', 'tointer': 'select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row . select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season } ; hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season } } = true

select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row . select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'oberbayern a_7': 7, 'bsc sendling_8': 8, 'season_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'oberbayern a_11': 11, 'fc oberau_12': 12, 'season_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'oberbayern a_7': 'oberbayern a', 'bsc sendling_8': 'bsc sendling', 'season_9': 'season', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'oberbayern a_11': 'oberbayern a', 'fc oberau_12': 'fc oberau', 'season_13': 'season'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'oberbayern a_7': [0], 'bsc sendling_8': [0], 'season_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'oberbayern a_11': [1], 'fc oberau_12': [1], 'season_13': [3]}

['season', 'oberbayern a', 'oberbayern b', 'niederbayern', 'schwaben', 'oberpfalz']

[['1951 - 52', 'sc münchen - süd', 'spvgg helios münchen', 'spvgg plattling', 'fc kempten', 'sv mitterteich'], ['1952 - 53', 'mtv ingolstadt', 'fc penzberg', 'spvgg deggendorf', 'tsv kottern', 'sv mitterteich'], ['1953 - 54', 'bsc sendling', 'tsv raubling', 'sv saal', 'spvgg kaufbeuren', 'tv sulzbach - rosenberg'], ['1954 - 55', 'tsg pasing', 'asv dachau', 'sv saal', 'fc memmingen', 'fc maxhütte - haidhof'], ['1955 - 56', 'fc bayern munich ii', 'sv aubing', '1 . fc passau', 'fc kempten', 'fc schwandorf'], ['1956 - 57', 'tsg pasing', 'sc 1906 münchen', '1 . fc passau', 'fc kempten', 'fc maxhütte - haidhof'], ['1957 - 58', 'spvgg helios münchen', 'sv aubing', '1 . fc passau', 'spvgg kaufbeuren', 'spvgg vohenstrauß'], ['1958 - 59', 'tsv 1860 munich ii', 'fsv pfaffenhofen', 'spvgg deggendorf', 'fc memmingen', 'turnerschaft regensburg'], ['1959 - 60', 'tsv 1860 rosenheim', 'tsg pasing', 'spvgg landshut', 'tsv kottern', 'tus rosenberg'], ['1960 - 61', 'fc oberau', 'esv ingolstadt', '1 . fc passau', 'tsv königsbrunn', 'tv wackersdorf'], ['1961 - 62', 'wacker burghausen', 'fc wacker münchen', 'sv saal', 'bc augsburg ii', 'jahn regensburg ii']]

list of england national rugby union team results 1990 - 99

https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1990%E2%80%9399

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178534-1.html.csv

superlative

argentina was the opposing team that recorded the highest score against the england national rugby union team .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'against'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; against }'}, 'opposing teams'], 'result': 'argentina', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; against } ; opposing teams }'}, 'argentina'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; against } ; opposing teams } ; argentina } = true', 'tointer': 'select the row whose against record of all rows is maximum . the opposing teams record of this row is argentina .'}

eq { hop { argmax { all_rows ; against } ; opposing teams } ; argentina } = true

select the row whose against record of all rows is maximum . the opposing teams record of this row is argentina .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'against_5': 5, 'opposing teams_6': 6, 'argentina_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'against_5': 'against', 'opposing teams_6': 'opposing teams', 'argentina_7': 'argentina'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'against_5': [0], 'opposing teams_6': [1], 'argentina_7': [2]}

['opposing teams', 'against', 'date', 'venue', 'status']

[['ireland', '0', '20 / 01 / 1990', 'twickenham , london', 'five nations'], ['france', '7', '03 / 02 / 1990', 'parc des princes , paris', 'five nations'], ['wales', '6', '17 / 02 / 1990', 'twickenham , london', 'five nations'], ['scotland', '13', '17 / 03 / 1990', 'murrayfield , edinburgh', 'five nations'], ['argentina', '12', '28 / 07 / 1990', 'vélez sársfield , buenos aires', 'first test'], ['argentina', '15', '04 / 08 / 1990', 'vélez sársfield , buenos aires', 'second test'], ['argentina', '0', '03 / 11 / 1990', 'twickenham , london', 'test match']]

2008 iaaf world indoor championships - women 's 400 metres

https://en.wikipedia.org/wiki/2008_IAAF_World_Indoor_Championships_%E2%80%93_Women%27s_400_metres

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16196238-4.html.csv

count

two of the atheletes listed were from russia .

{'scope': 'all', 'criterion': 'equal', 'value': 'russia', 'result': '2', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'russia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to russia .', 'tostr': 'filter_eq { all_rows ; country ; russia }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; russia } }', 'tointer': 'select the rows whose country record fuzzily matches to russia . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; russia } } ; 2 } = true', 'tointer': 'select the rows whose country record fuzzily matches to russia . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; country ; russia } } ; 2 } = true

select the rows whose country record fuzzily matches to russia . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'russia_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'russia_6': 'russia', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'russia_6': [0], '2_7': [2]}

['lane', 'name', 'country', 'mark', 'react']

[['6', 'olesya zykina', 'russia', '51.09 wl', '0.297'], ['5', 'natalya nazarova', 'russia', '51.10 sb', '0.247'], ['3', 'shareese woods', 'united states', '51.41 pb', '0.237'], ['4', 'antonina yefremova', 'ukraine', '51.53 pb', '0.147'], ['2', 'angela morosanu', 'romania', '53.07', '0.269'], ['1', 'moushaumi robinson', 'united states', '53.10', '0.257']]

list of romanian counties by foreign trade

https://en.wikipedia.org/wiki/List_of_Romanian_counties_by_foreign_trade

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24239748-2.html.csv

majority

a majority of counties have double digit percentages of total exports .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'percent of total imports', '10'], 'result': True, 'ind': 0, 'tointer': 'for the percent of total imports records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; percent of total imports ; 10 } = true'}

most_greater { all_rows ; percent of total imports ; 10 } = true

for the percent of total imports records of all rows , most of them are greater than 10 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'percent of total imports_3': 3, '10_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'percent of total imports_3': 'percent of total imports', '10_4': '10'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'percent of total imports_3': [0], '10_4': [0]}

['county', 'exports ( us mil )', 'percent of total exports', 'imports ( us mil )', 'percent of total imports']

[['bucharest - ilfov', '8001.2', '19.2 %', '26557.8', '39.8 %'], ['sud - muntenia', '6300 , 7', '15.1 %', '6785.5', '10.2 %'], ['vest', '6270.2', '15.0 %', '6597.6', '9.9 %'], ['sud - est', '5762', '13.8 %', '7501.9', '11.2 %'], ['centru', '5338', '12.8 %', '7.879.4', '11.8 %'], ['nord - vest', '4726.6', '11.3 %', '6999.1', '10.5 %'], ['sud - vest oltenia', '3226.2', '7.7 %', '2007.8', '3.0 %']]

antonio ng

https://en.wikipedia.org/wiki/Antonio_Ng

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14834801-1.html.csv

aggregation

antonio ng received a combined total of 66617 list votes as a candidate in elections .

{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '66617', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'list votes'], 'result': '66617', 'ind': 0, 'tostr': 'sum { all_rows ; list votes }'}, '66617'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; list votes } ; 66617 } = true', 'tointer': 'the sum of the list votes record of all rows is 66617 .'}

round_eq { sum { all_rows ; list votes } ; 66617 } = true

the sum of the list votes record of all rows is 66617 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'list votes_4': 4, '66617_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'list votes_4': 'list votes', '66617_5': '66617'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'list votes_4': [0], '66617_5': [1]}

['year', 'candidate', 'hare quota', 'mandate', 'list votes', 'list pct']

[['1992', 'antónio ng ( anmd )', '3412', '№ 4', '3412', '12.39 %'], ['1996', 'antónio ng ( amdp )', '6331', '№ 6', '6331', '8.73 %'], ['2001', 'antónio ng ( amdp )', '8481', '№ 1', '16961', '20.95 %'], ['2005', 'antónio ng ( amdp )', '11745', '№ 1', '23489', '18.80 %'], ['2009', 'antónio ng ( apmd )', '8212', '№ 3', '16424', '11.58 %']]

1972 denver broncos season

https://en.wikipedia.org/wiki/1972_Denver_Broncos_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17848578-1.html.csv

majority

most of the denver broncos games had an attendance above 51,000 .

{'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '51,000', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'attendance', '51,000'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are greater than 51,000 .', 'tostr': 'most_greater { all_rows ; attendance ; 51,000 } = true'}

most_greater { all_rows ; attendance ; 51,000 } = true

for the attendance records of all rows , most of them are greater than 51,000 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '51,000_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '51,000_4': '51,000'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '51,000_4': [0]}

['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']

[['1', 'september 17', 'houston oilers', 'w 30 - 17', 'mile high stadium', '1 - 0', '51656'], ['2', 'september 24', 'san diego chargers', 'l 14 - 37', 'san diego stadium', '1 - 1', '49048'], ['3', 'october 1', 'kansas city chiefs', 'l 24 - 45', 'mile high stadium', '1 - 2', '51656'], ['4', 'october 8', 'cincinnati bengals', 'l 10 - 21', 'riverfront stadium', '1 - 3', '55812'], ['5', 'october 15', 'minnesota vikings', 'l 20 - 23', 'mile high stadium', '1 - 4', '51656'], ['6', 'october 22', 'oakland raiders', 'w 30 - 23', 'oakland - alameda county coliseum', '2 - 4', '53551'], ['7', 'october 29', 'cleveland browns', 'l 20 - 27', 'mile high stadium', '2 - 5', '51656'], ['8', 'november 5', 'new york giants', 'l 17 - 29', 'yankee stadium', '2 - 6', '62689'], ['9', 'november 12', 'los angeles rams', 'w 16 - 10', 'los angeles memorial coliseum', '3 - 6', '65398'], ['10', 'november 19', 'oakland raiders', 'l 20 - 37', 'mile high stadium', '3 - 7', '51656'], ['11', 'november 26', 'atlanta falcons', 'l 20 - 23', 'atlanta - fulton county stadium', '3 - 8', '58850'], ['12', 'december 3', 'kansas city chiefs', 'l 21 - 24', 'arrowhead stadium', '3 - 9', '66725'], ['13', 'december 10', 'san diego chargers', 'w 38 - 13', 'mile high stadium', '4 - 9', '51478'], ['14', 'december 17', 'new england patriots', 'w 45 - 21', 'mile high stadium', '5 - 9', '51656']]

jack fairman

https://en.wikipedia.org/wiki/Jack_Fairman

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235888-1.html.csv

unique

1953 was the only year that jack fairman drove with a hwm 53 type chassis .

{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'hwm 53', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'hwm 53'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to hwm 53 .', 'tostr': 'filter_eq { all_rows ; chassis ; hwm 53 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; chassis ; hwm 53 } }', 'tointer': 'select the rows whose chassis record fuzzily matches to hwm 53 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'hwm 53'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to hwm 53 .', 'tostr': 'filter_eq { all_rows ; chassis ; hwm 53 }'}, 'year'], 'result': '1953', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; chassis ; hwm 53 } ; year }'}, '1953'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; chassis ; hwm 53 } ; year } ; 1953 }', 'tointer': 'the year record of this unqiue row is 1953 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; chassis ; hwm 53 } } ; eq { hop { filter_eq { all_rows ; chassis ; hwm 53 } ; year } ; 1953 } } = true', 'tointer': 'select the rows whose chassis record fuzzily matches to hwm 53 . there is only one such row in the table . the year record of this unqiue row is 1953 .'}

and { only { filter_eq { all_rows ; chassis ; hwm 53 } } ; eq { hop { filter_eq { all_rows ; chassis ; hwm 53 } ; year } ; 1953 } } = true

select the rows whose chassis record fuzzily matches to hwm 53 . there is only one such row in the table . the year record of this unqiue row is 1953 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'chassis_7': 7, 'hwm 53_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1953_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'chassis_7': 'chassis', 'hwm 53_8': 'hwm 53', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1953_10': '1953'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'chassis_7': [0], 'hwm 53_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1953_10': [3]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1953', 'hw motors', 'hwm 53', 'alta', '0'], ['1953', 'connaught engineering', 'connaught type a', 'lea francis', '0'], ['1955', 'connaught engineering', 'connaught type b', 'alta', '0'], ['1956', 'connaught engineering', 'connaught type b', 'alta', '5'], ['1957', 'owen racing organisation', 'brm p25', 'brm', '0'], ['1958', 'bc ecclestone', 'connaught type b', 'alta', '0'], ['1958', 'cooper car company', 'cooper t45', 'coventry climax', '0'], ['1959', 'high efficiency motors', 'cooper t45', 'coventry climax', '0'], ['1959', 'high efficiency motors', 'cooper t45', 'maserati', '0'], ['1960', 'ct atkins', 'cooper t51', 'coventry climax', '0'], ['1961', 'rob walker racing', 'ferguson p99', 'coventry climax', '0'], ['1961', 'fred tuck cars', 'cooper t45', 'coventry climax', '0']]

1951 vfl season

https://en.wikipedia.org/wiki/1951_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10701914-10.html.csv

aggregation

in the 1951 vfl season , the average score for home teams was 10 .

{'scope': 'all', 'col': '2', 'type': 'average', 'result': '10', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'home team score'], 'result': '10', 'ind': 0, 'tostr': 'avg { all_rows ; home team score }'}, '10'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; home team score } ; 10 } = true', 'tointer': 'the average of the home team score record of all rows is 10 .'}

round_eq { avg { all_rows ; home team score } ; 10 } = true

the average of the home team score record of all rows is 10 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'home team score_4': 4, '10_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'home team score_4': 'home team score', '10_5': '10'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'home team score_4': [0], '10_5': [1]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['st kilda', '7.7 ( 49 )', 'south melbourne', '13.15 ( 93 )', 'junction oval', '21000', '30 june 1951'], ['collingwood', '10.12 ( 72 )', 'footscray', '12.5 ( 77 )', 'victoria park', '25000', '30 june 1951'], ['carlton', '14.20 ( 104 )', 'north melbourne', '5.10 ( 40 )', 'princes park', '22000', '30 june 1951'], ['fitzroy', '10.14 ( 74 )', 'hawthorn', '11.9 ( 75 )', 'brunswick street oval', '8500', '7 july 1951'], ['essendon', '10.14 ( 74 )', 'richmond', '10.10 ( 70 )', 'windy hill', '30000', '7 july 1951'], ['melbourne', '7.7 ( 49 )', 'geelong', '12.12 ( 84 )', 'mcg', '22500', '7 july 1951']]

1957 argentine grand prix

https://en.wikipedia.org/wiki/1957_Argentine_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1122148-1.html.csv

majority

in the 1957 argentine grand prix , the majority of teams that had to retire due to a clutch problem finished more than 30 laps .

{'scope': 'subset', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '30', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'clutch'}}

{'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time / retired', 'clutch'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; time / retired ; clutch }', 'tointer': 'select the rows whose time / retired record fuzzily matches to clutch .'}, 'laps', '30'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose time / retired record fuzzily matches to clutch . for the laps records of these rows , most of them are greater than 30 .', 'tostr': 'most_greater { filter_eq { all_rows ; time / retired ; clutch } ; laps ; 30 } = true'}

most_greater { filter_eq { all_rows ; time / retired ; clutch } ; laps ; 30 } = true

select the rows whose time / retired record fuzzily matches to clutch . for the laps records of these rows , most of them are greater than 30 .

2

2

{'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'time / retired_4': 4, 'clutch_5': 5, 'laps_6': 6, '30_7': 7}

{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'time / retired_4': 'time / retired', 'clutch_5': 'clutch', 'laps_6': 'laps', '30_7': '30'}

{'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'time / retired_4': [0], 'clutch_5': [0], 'laps_6': [1], '30_7': [1]}

['driver', 'constructor', 'laps', 'time / retired', 'grid']

[['juan manuel fangio', 'maserati', '100', '3:00:55.9', '2'], ['jean behra', 'maserati', '100', '+ 18.3 secs', '3'], ['carlos menditeguy', 'maserati', '99', '+ 1 lap', '8'], ['harry schell', 'maserati', '98', '+ 2 laps', '9'], ['alfonso de portago josé froilán gonzález', 'ferrari', '98', '+ 2 laps', '10'], ['cesare perdisa peter collins wolfgang von trips', 'ferrari', '98', '+ 2 laps', '11'], ['jo bonnier', 'maserati', '95', '+ 5 laps', '13'], ['stirling moss', 'maserati', '93', '+ 7 laps', '1'], ['alessandro de tomaso', 'ferrari', '91', '+ 9 laps', '12'], ['luigi piotti', 'maserati', '90', '+ 10 laps', '14'], ['eugenio castellotti', 'ferrari', '75', 'wheel', '4'], ['mike hawthorn', 'ferrari', '35', 'clutch', '7'], ['luigi musso', 'ferrari', '31', 'clutch', '6'], ['peter collins', 'ferrari', '26', 'clutch', '5']]

television in italy

https://en.wikipedia.org/wiki/Television_in_Italy

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15887683-19.html.csv

unique

for television in italy , when the content is programmi per adulti 24h / 24 , the only time the television service is boy & boy is when the n degree is 992 .

{'scope': 'subset', 'row': '11', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'boy & boy', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'programmi per adulti 24h / 24'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'content', 'programmi per adulti 24h / 24'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; content ; programmi per adulti 24h / 24 }', 'tointer': 'select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 .'}, 'television service', 'boy & boy'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 . among these rows , select the rows whose television service record fuzzily matches to boy & boy .', 'tostr': 'filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } }', 'tointer': 'select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 . among these rows , select the rows whose television service record fuzzily matches to boy & boy . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'content', 'programmi per adulti 24h / 24'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; content ; programmi per adulti 24h / 24 }', 'tointer': 'select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 .'}, 'television service', 'boy & boy'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 . among these rows , select the rows whose television service record fuzzily matches to boy & boy .', 'tostr': 'filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy }'}, 'n degree'], 'result': '992', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } ; n degree }'}, '992'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } ; n degree } ; 992 }', 'tointer': 'the n degree record of this unqiue row is 992 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } } ; eq { hop { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } ; n degree } ; 992 } } = true', 'tointer': 'select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 . among these rows , select the rows whose television service record fuzzily matches to boy & boy . there is only one such row in the table . the n degree record of this unqiue row is 992 .'}

and { only { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } } ; eq { hop { filter_eq { filter_eq { all_rows ; content ; programmi per adulti 24h / 24 } ; television service ; boy & boy } ; n degree } ; 992 } } = true

select the rows whose content record fuzzily matches to programmi per adulti 24h / 24 . among these rows , select the rows whose television service record fuzzily matches to boy & boy . there is only one such row in the table . the n degree record of this unqiue row is 992 .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'content_8': 8, 'programmi per adulti 24h / 24_9': 9, 'television service_10': 10, 'boy&boy_11': 11, 'eq_4': 4, 'num_hop_3': 3, 'n degree_12': 12, '992_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'content_8': 'content', 'programmi per adulti 24h / 24_9': 'programmi per adulti 24h / 24', 'television service_10': 'television service', 'boy&boy_11': 'boy & boy', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'n degree_12': 'n degree', '992_13': '992'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'content_8': [0], 'programmi per adulti 24h / 24_9': [0], 'television service_10': [1], 'boy&boy_11': [1], 'eq_4': [5], 'num_hop_3': [4], 'n degree_12': [3], '992_13': [4]}

['n degree', 'television service', 'country', 'language', 'content', 'dar', 'hdtv', 'ppv', 'package / option']

[['981', 'contotv 1', 'italy', 'italian', 'general television', '4:3', 'no', 'yes', 'qualsiasi'], ['982', 'contotv 2', 'italy', 'italian', 'general television', '4:3', 'no', 'yes', 'qualsiasi'], ['983', 'contotv 3', 'italy', 'italian', 'general television', '16:9', 'no', 'no', 'qualsiasi'], ['984', 'contotv 4', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'no', 'qualsiasi'], ['985', 'contotv 5', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'no', 'qualsiasi'], ['987', 'teleitalia', 'italy', 'italian', 'general television', '4:3', 'no', 'yes', 'qualsiasi ( fta )'], ['988', 'teleitalia spot', 'italy', 'italian', 'general television', '4:3', 'no', 'yes', 'qualsiasi ( fta )'], ['989', 'd - xtv', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'yes', 'qualsiasi'], ['990', 'r - light', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'yes', 'qualsiasi'], ['991', 'sct', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'yes', 'qualsiasi'], ['992', 'boy & boy', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'yes', 'qualsiasi'], ['993', 'privè', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'yes', 'qualsiasi'], ['994', 'themex', 'italy', 'italian', 'programmi per adulti 24h / 24', '4:3', 'no', 'yes', 'qualsiasi']]

list of tallest buildings in philadelphia

https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Philadelphia

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12587248-3.html.csv

ordinal

philadelphia city hall is the tallest building in philadelphia among those whose architect is john mcarthur , jr .

{'scope': 'subset', 'row': '5', 'col': '4', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'john mcarthur , jr'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'architect', 'john mcarthur , jr'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; architect ; john mcarthur , jr }', 'tointer': 'select the rows whose architect record fuzzily matches to john mcarthur , jr .'}, 'height ft ( m )', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; architect ; john mcarthur , jr } ; height ft ( m ) ; 1 }'}, 'name'], 'result': 'philadelphia city hall', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; architect ; john mcarthur , jr } ; height ft ( m ) ; 1 } ; name }'}, 'philadelphia city hall'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; architect ; john mcarthur , jr } ; height ft ( m ) ; 1 } ; name } ; philadelphia city hall } = true', 'tointer': 'select the rows whose architect record fuzzily matches to john mcarthur , jr . select the row whose height ft ( m ) record of these rows is 1st maximum . the name record of this row is philadelphia city hall .'}

eq { hop { nth_argmax { filter_eq { all_rows ; architect ; john mcarthur , jr } ; height ft ( m ) ; 1 } ; name } ; philadelphia city hall } = true

select the rows whose architect record fuzzily matches to john mcarthur , jr . select the row whose height ft ( m ) record of these rows is 1st maximum . the name record of this row is philadelphia city hall .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'architect_6': 6, 'john mcarthur , jr_7': 7, 'height ft (m)_8': 8, '1_9': 9, 'name_10': 10, 'philadelphia city hall_11': 11}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'architect_6': 'architect', 'john mcarthur , jr_7': 'john mcarthur , jr', 'height ft (m)_8': 'height ft ( m )', '1_9': '1', 'name_10': 'name', 'philadelphia city hall_11': 'philadelphia city hall'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'architect_6': [0], 'john mcarthur , jr_7': [0], 'height ft (m)_8': [1], '1_9': [1], 'name_10': [2], 'philadelphia city hall_11': [3]}

['name', 'street address', 'years as tallest', 'height ft ( m )', 'floors', 'architect']

[['independence hall', '520 chestnut street', '1748 - 1754', '134 ( 41 )', '-', 'edmund woolley and andrew hamilton'], ['christ church', '20 north american street', '1754 - 1856', '196 ( 60 )', '-', 'robert smith'], ['tenth presbyterian church', '17th & spruce streets', '1856 - 1900', '250 ( 76 )', '-', 'john mcarthur , jr'], ['north american building', '121 south broad street', '1900 - 1901', '267 ( 81 )', '21', 'james h windrim'], ['philadelphia city hall', 'broad & market streets', '1901 - 1987', '548 ( 167 )', '9', 'john mcarthur , jr'], ['one liberty place', '1650 market street', '1987 - 2008', '945 ( 288 )', '61', 'helmut jahn'], ['comcast center', '1701 john f kennedy boulevard', '2008 - present', '975 ( 297 )', '57', 'robert a m stern architects']]

1983 senior pga tour

https://en.wikipedia.org/wiki/1983_Senior_PGA_Tour

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11622862-1.html.csv

superlative

the winner of the senior tournament players championship received the biggest purse .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'purse'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; purse }'}, 'tournament'], 'result': 'senior tournament players championship', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; purse } ; tournament }'}, 'senior tournament players championship'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; purse } ; tournament } ; senior tournament players championship } = true', 'tointer': 'select the row whose purse record of all rows is maximum . the tournament record of this row is senior tournament players championship .'}

eq { hop { argmax { all_rows ; purse } ; tournament } ; senior tournament players championship } = true

select the row whose purse record of all rows is maximum . the tournament record of this row is senior tournament players championship .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'purse_5': 5, 'tournament_6': 6, 'senior tournament players championship_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'purse_5': 'purse', 'tournament_6': 'tournament', 'senior tournament players championship_7': 'senior tournament players championship'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'purse_5': [0], 'tournament_6': [1], 'senior tournament players championship_7': [2]}

['date', 'tournament', 'location', 'purse', 'winner', 'score', '1st prize']

[['mar 20', 'greater daytona senior classic', 'florida', '150000', 'gene littler ( 1 )', '203 ( - 13 )', '25000'], ['may 22', 'hall of fame tournament', 'north carolina', '150000', 'rod funseth ( 1 )', '198 ( - 18 )', '25000'], ['jun 5', 'gatlin brothers seniors golf classic', 'nevada', '200000', 'don january ( 6 )', '208 ( - 8 )', '33500'], ['jun 12', 'senior tournament players championship', 'ohio', '250000', 'miller barber ( 7 )', '278 ( - 10 )', '40000'], ['jun 26', 'peter jackson champions', 'canada', '200000', 'don january ( 7 )', '274 ( - 10 )', '33250'], ['jul 3', 'marlboro classic', 'massachusetts', '150000', 'don january ( 8 )', '273 ( - 11 )', '25000'], ['jul 10', "greater syracuse senior 's pro classic", 'new york', '150000', 'gene littler ( 2 )', '275 ( - 9 )', '25000'], ['jul 17', 'merrill lynch / golf digest commemorative pro - am', 'rhode island', '150000', 'miller barber ( 8 )', '200 ( - 16 )', '25000'], ['jul 25', 'us senior open', 'minnesota', '175000', 'billy casper ( 3 )', '288 ( 4 )', '30566'], ['aug 21', 'denver post champions of golf', 'colorado', '150000', 'don january ( 9 )', '271 ( - 17 )', '25000'], ['sep 4', 'citizens union senior golf classic', 'kentucky', '150000', 'don january ( 10 )', '269 ( - 19 )', '25000'], ['sep 25', 'world seniors invitational', 'north carolina', '152000', 'doug sanders ( 1 )', '283 ( - 5 )', '25000'], ['oct 2', 'united virginia bank seniors', 'virginia', '150000', 'miller barber ( 9 )', '211 ( - 5 )', '25000'], ['oct 16', 'suntree classic', 'florida', '135000', 'don january ( 11 )', '274 ( - 14 )', '22500'], ['oct 23', 'hilton head seniors international', 'south carolina', '150000', 'miller barber ( 10 )', '281 ( - 7 )', '25000'], ['dec 4', 'boca grove seniors classic', 'florida', '150000', 'arnold palmer ( 5 )', '271 ( - 17 )', '25000']]

henlopen conference

https://en.wikipedia.org/wiki/Henlopen_Conference

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-17.html.csv

count

two teams in the henlopen conference made the divisional playoffs .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'div i playoffs', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season outcome', 'div i playoffs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season outcome record fuzzily matches to div i playoffs .', 'tostr': 'filter_eq { all_rows ; season outcome ; div i playoffs }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; season outcome ; div i playoffs } }', 'tointer': 'select the rows whose season outcome record fuzzily matches to div i playoffs . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; season outcome ; div i playoffs } } ; 2 } = true', 'tointer': 'select the rows whose season outcome record fuzzily matches to div i playoffs . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; season outcome ; div i playoffs } } ; 2 } = true

select the rows whose season outcome record fuzzily matches to div i playoffs . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'season outcome_5': 5, 'div i playoffs_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'season outcome_5': 'season outcome', 'div i playoffs_6': 'div i playoffs', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'season outcome_5': [0], 'div i playoffs_6': [0], '2_7': [2]}

['school', 'team', 'division record', 'overall record', 'season outcome']

[['sussex central', 'golden knights', '6 - 0', '7 - 4', 'loss in first round of div i playoffs'], ['dover', 'senators', '5 - 1', '8 - 4', 'loss in semi - finals of div i playoffs'], ['cape henlopen', 'vikings', '4 - 2', '8 - 2', 'failed to make playoffs'], ['caesar rodney', 'riders', '3 - 3', '3 - 7', 'failed to make playoffs'], ['smyrna', 'eagles', '2 - 4', '5 - 5', 'failed to make playoffs'], ['sussex tech', 'ravens', '1 - 5', '4 - 6', 'failed to make playoffs'], ['milford', 'buccaneers', '0 - 6', '1 - 9', 'failed to make playoffs']]

2007 tampa bay storm season

https://en.wikipedia.org/wiki/2007_Tampa_Bay_Storm_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11486671-4.html.csv

majority

most of the players from tampa bay storm had at least one td in the 2007 season .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '1', 'subset': None}

{'func': 'most_greater_eq', 'args': ['all_rows', "td 's", '1'], 'result': True, 'ind': 0, 'tointer': "for the td 's records of all rows , most of them are greater than or equal to 1 .", 'tostr': "most_greater_eq { all_rows ; td 's ; 1 } = true"}

most_greater_eq { all_rows ; td 's ; 1 } = true

for the td 's records of all rows , most of them are greater than or equal to 1 .

1

1

{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, "td 's_3": 3, '1_4': 4}

{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', "td 's_3": "td 's", '1_4': '1'}

{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], "td 's_3": [0], '1_4': [0]}

['place', 'player name', 'yards', "td 's", 'long']

[['1', 'torrance marshall', '107', '17', '9'], ['2', 'rodney filer', '96', '9', '14'], ['3', 'marvin brown', '43', '1', '23'], ['4', 'tt toliver', '24', '0', '13'], ['5', 'john kaleo', '16', '1', '7'], ['6', 'brett dietz', '7', '2', '4'], ['7', 'stoney case', '4', '2', '3'], ['8', 'clenton crossley', '3', '0', '3'], ['9', 'rod williams', '1', '0', '1'], ['10', 'jarrod penright', '3', '0', '1']]

humor magazine

https://en.wikipedia.org/wiki/Humor_magazine

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2266990-2.html.csv

count

there are two magazine that hold the classification of absurdism .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'absurdism', 'result': '2', 'col': '7', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'classification', 'absurdism'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose classification record fuzzily matches to absurdism .', 'tostr': 'filter_eq { all_rows ; classification ; absurdism }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; classification ; absurdism } }', 'tointer': 'select the rows whose classification record fuzzily matches to absurdism . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; classification ; absurdism } } ; 2 } = true', 'tointer': 'select the rows whose classification record fuzzily matches to absurdism . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; classification ; absurdism } } ; 2 } = true

select the rows whose classification record fuzzily matches to absurdism . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'classification_5': 5, 'absurdism_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'classification_5': 'classification', 'absurdism_6': 'absurdism', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'classification_5': [0], 'absurdism_6': [0], '2_7': [2]}

['title', 'language', 'country', 'years published', 'frequency', 'medium', 'classification']

[['fish rap live !', 'english', 'usa', '1985 - 1988 , 1990 - present', '9 per year', 'paper', 'satire , absurdism , theme issues'], ['harvard lampoon', 'english', 'usa', '1876 - present', 'five per year', 'paper', 'satire'], ['krokodil', 'russian', 'russia', '1922 - 1991 , 2005 - present', 'weekly', 'paper', 'satire'], ['le canard enchaîné', 'french', 'france', '1915 - present', 'weekly', 'paper', 'satire'], ['mad', 'english', 'usa', '1952 - present', 'monthly ( 1952 - 2009 ) , quarterly ( 2009 - )', 'paper', 'satire , comics'], ['princeton tiger', 'english', 'usa', '1882 - present', 'quarterly', 'paper ( 1882 - ) online ( 2009 - )', 'satire'], ['private eye', 'english', 'uk', '1961 - present', 'biweekly', 'paper', 'satire'], ['rumpus', 'english', 'usa', '1992 - present', 'six per year', 'paper', 'satire / tabloid'], ['the wittenburg door', 'english', 'usa', '1971 - present', 'bimonthly', 'paper', 'christian satire'], ['the yale record', 'english', 'usa', '1872 - present', '8 per year', 'paper', 'satire , theme issues'], ['jester of columbia', 'english', 'usa', '1991 - 1997 , 2005 - present', 'quarterly', 'paper', 'satire , absurdism']]

orlando magic all - time roster

https://en.wikipedia.org/wiki/Orlando_Magic_all-time_roster

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15621965-14.html.csv

count

8 players are listed in the orlando magic all - time roster .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '8', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'player'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is arbitrary .', 'tostr': 'filter_all { all_rows ; player }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; player } }', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; player } } ; 8 } = true', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 8 .'}

eq { count { filter_all { all_rows ; player } } ; 8 } = true

select the rows whose player record is arbitrary . the number of such rows is 8 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'player_5': 5, '8_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'player_5': 'player', '8_6': '8'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'player_5': [0], '8_6': [2]}

['player', 'no', 'nationality', 'position', 'years in orlando', 'school / club team']

[['victor oladipo', '5', 'united states', 'guard', '2013 - present', 'indiana'], ['jawann oldham', '55', 'united states', 'center', '1989 - 1990', 'seattle'], ['kevin ollie', '3', 'united states', 'guard', '1998', 'connecticut'], ["shaquille o'neal", '32', 'united states', 'center', '1992 - 1996', 'louisiana state'], ['daniel orton', '21', 'united states', 'center', '2010 - 2012', 'kentucky'], ['bo outlaw', '45', 'united states', 'forward - center', '1997 - 2001', 'houston'], ['bo outlaw', '45', 'united states', 'forward - center', '2005 - 2008', 'houston'], ['doug overton', '11', 'united states', 'guard', '1998 - 1999', 'la salle']]

eurobasket 1967

https://en.wikipedia.org/wiki/EuroBasket_1967

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13843829-3.html.csv

aggregation

the average number of wins at eurobasket 1967 was 3.5 .

{'scope': 'all', 'col': '3', 'type': 'average', 'result': '3.5', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'wins'], 'result': '3.5', 'ind': 0, 'tostr': 'avg { all_rows ; wins }'}, '3.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; wins } ; 3.5 } = true', 'tointer': 'the average of the wins record of all rows is 3.5 .'}

round_eq { avg { all_rows ; wins } ; 3.5 } = true

the average of the wins record of all rows is 3.5 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'wins_4': 4, '3.5_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '3.5_5': '3.5'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'wins_4': [0], '3.5_5': [1]}

['pos', 'matches', 'wins', 'loses', 'results', 'points', 'diff']

[['1', '7', '6', '1', '550:461', '12', '+ 89'], ['2', '7', '6', '1', '554:485', '12', '+ 69'], ['3', '7', '5', '2', '479:449', '10', '+ 30'], ['4', '7', '4', '3', '493:497', '8', '4'], ['5', '7', '4', '3', '523:507', '8', '+ 16'], ['6', '7', '2', '5', '526:579', '4', '53'], ['7', '7', '1', '6', '500:581', '2', '81'], ['8', '7', '0', '7', '454:570', '0', '116']]

1965 american football league draft

https://en.wikipedia.org/wiki/1965_American_Football_League_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18652198-4.html.csv

comparative

during the 1965 afl draft , gus otto was drafted before otis taylor .

{'row_1': '3', 'row_2': '5', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'gus otto'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to gus otto .', 'tostr': 'filter_eq { all_rows ; player ; gus otto }'}, 'pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; gus otto } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to gus otto . take the pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'otis taylor'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to otis taylor .', 'tostr': 'filter_eq { all_rows ; player ; otis taylor }'}, 'pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; otis taylor } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to otis taylor . take the pick record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; gus otto } ; pick } ; hop { filter_eq { all_rows ; player ; otis taylor } ; pick } } = true', 'tointer': 'select the rows whose player record fuzzily matches to gus otto . take the pick record of this row . select the rows whose player record fuzzily matches to otis taylor . take the pick record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; player ; gus otto } ; pick } ; hop { filter_eq { all_rows ; player ; otis taylor } ; pick } } = true

select the rows whose player record fuzzily matches to gus otto . take the pick record of this row . select the rows whose player record fuzzily matches to otis taylor . take the pick record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'gus otto_8': 8, 'pick_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'otis taylor_12': 12, 'pick_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'gus otto_8': 'gus otto', 'pick_9': 'pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'otis taylor_12': 'otis taylor', 'pick_13': 'pick'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'gus otto_8': [0], 'pick_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'otis taylor_12': [1], 'pick_13': [3]}

['pick', 'team', 'player', 'position', 'college']

[['25', 'denver broncos', 'george donnelly', 'defensive back', 'illinois'], ['26', 'houston oilers', 'bobby maples', 'center', 'baylor'], ['27', 'oakland raiders', 'gus otto', 'linebacker', 'missouri'], ['28', 'new york jets', 'bob schweickert', 'quarterback', 'virginia tech'], ['29', 'kansas city chiefs', 'otis taylor', 'linebacker', 'prairie view a & m'], ['30', 'san diego chargers', 'steve tensi', 'quarterback', 'florida state'], ['31', 'boston patriots', 'ellis johnson', 'halfback', 'southeastern louisiana'], ['32', 'kansas city chiefs ( from buffalo bills )', 'frank pitts', 'wide receiver', 'saginaw valley state']]

sports in st. louis

https://en.wikipedia.org/wiki/Sports_in_St._Louis

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21564794-3.html.csv

aggregation

out of the listed former st. louis sports teams , the teams won an average of .3125 championships .

{'scope': 'all', 'col': '7', 'type': 'average', 'result': '.3125', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'championships in st louis'], 'result': '.3125', 'ind': 0, 'tostr': 'avg { all_rows ; championships in st louis }'}, '.3125'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; championships in st louis } ; .3125 } = true', 'tointer': 'the average of the championships in st louis record of all rows is .3125 .'}

round_eq { avg { all_rows ; championships in st louis } ; .3125 } = true

the average of the championships in st louis record of all rows is .3125 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'championships in st louis_4': 4, '.3125_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'championships in st louis_4': 'championships in st louis', '.3125_5': '.3125'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'championships in st louis_4': [0], '.3125_5': [1]}

['team', 'sport', 'league', 'established', 'began in st louis', 'venue', 'championships in st louis', 'left st louis']

[['st louis stampede', 'arena football', 'arena football league', '1987', '1994', 'scottrade center', '0', '1995'], ['st louis browns', 'baseball', 'american league', '1894', '1902', "sportsman 's park", '0', '1954'], ['st louis stars', 'baseball', 'negro american league', '1937', '1939', 'stars park', '0', '1939'], ['st louis terriers', 'baseball', 'federal league', '1914', '1914', "handlan 's park", '0', '1915'], ['st louis maroons', 'baseball', 'national league', '1884', '1884', 'union base ball park', '0', '1886'], ['st louis stars', 'baseball', 'negro national league', '1922', '1931', 'stars park', '3 ( 1928 , 1930 , 1931 )', '1931'], ['spirits of st louis', 'basketball', 'american basketball association', '1967', '1974', 'st louis arena', '0', '1976'], ['st louis hawks', 'basketball', 'national basketball association', '1946', '1955', 'kiel auditorium', '1 ( 1958 )', '1968'], ['st louis bombers', 'basketball', 'national basketball association', '1946', '1950', 'st louis arena', '0', '1950'], ['st louis cardinals', 'football', 'national football league', '1898', '1960', 'busch stadium', '0', '1988'], ['st louis all stars', 'football', 'national football league', '1923', '1923', "sportsman 's park", '0', '1923'], ['st louis gunners', 'football', 'national football league', '1931', '1931', 'st louis national guard armory', '0', '1934'], ['missouri river otters', 'hockey', 'united hockey league', '1991', '1999', 'family arena', '0', '2006'], ['st louis eagles', 'hockey', 'national hockey league', '1917', '1934', 'st louis arena', '0', '1936'], ['st louis ambush', 'indoor soccer', 'national professional soccer league', '1984', '1992', 'st louis arena / scottrade center', '1 ( 1995 )', '2000'], ['st louis steamers / st louis storm', 'indoor soccer', 'major indoor soccer league', '1977', '1979', 'st louis arena', '0', '1992']]

list of r. l. stine 's the haunting hour episodes

https://en.wikipedia.org/wiki/List_of_R._L._Stine%27s_The_Haunting_Hour_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29196086-4.html.csv

ordinal

the grampires ( part 1 ) is the earliest episode in the r. l. stine 's the haunting hour series .

{'row': '1', 'col': '2', 'order': '1', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'no in season', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; no in season ; 1 }'}, 'title'], 'result': 'grampires ( part 1 )', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; no in season ; 1 } ; title }'}, 'grampires ( part 1 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; no in season ; 1 } ; title } ; grampires ( part 1 ) } = true', 'tointer': 'select the row whose no in season record of all rows is 1st minimum . the title record of this row is grampires ( part 1 ) .'}

eq { hop { nth_argmin { all_rows ; no in season ; 1 } ; title } ; grampires ( part 1 ) } = true

select the row whose no in season record of all rows is 1st minimum . the title record of this row is grampires ( part 1 ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'no in season_5': 5, '1_6': 6, 'title_7': 7, 'grampires (part 1)_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'no in season_5': 'no in season', '1_6': '1', 'title_7': 'title', 'grampires (part 1)_8': 'grampires ( part 1 )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'no in season_5': [0], '1_6': [0], 'title_7': [1], 'grampires (part 1)_8': [2]}

['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date']

[['41', '1', 'grampires ( part 1 )', 'neill fearnley', 'erik patterson & jessica scott', 'october 13 , 2012'], ['42', '2', 'grampires ( part 2 )', 'neill fearnley', 'erik patterson & jessica scott', 'october 13 , 2012'], ['43', '3', 'the cast', 'ken friss', 'craig s phillips & harold hayes jr', 'october 20 , 2012'], ['44', '4', 'the weeping woman', 'neill fearnley', 'harold hayes jr & craig s phillips', 'october 27 , 2012'], ['45', '5', 'intruders', 'ken friss', 'jack monaco', 'november 3 , 2012'], ['47', '7', 'red eye', 'ken friss', 'natalie lapointe & greg yolen', 'november 17 , 2012'], ['48', '8', 'my imaginary friend', 'james head', 'melody fox', 'november 24 , 2012'], ['49', '9', 'poof de fromage', 'ken friss', 'erik patterson & jessica scott', 'december 1 , 2012'], ['50', '10', 'the golem ( part 1 )', 'neill fearnley', 'jack monaco', 'december 8 , 2012'], ['51', '11', 'the golem ( part 2 )', 'neill fearnley', 'jack monaco', 'december 8 , 2012'], ['52', '12', 'the girl in the painting', 'ken friss', 'jack monaco', 'december 15 , 2012'], ['53', '13', 'checking out', 'james head', 'melody fox', 'january 19 , 2013']]

2003 cricket world cup statistics

https://en.wikipedia.org/wiki/2003_Cricket_World_Cup_statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11611293-10.html.csv

superlative

m kaif made the most catches in the 2003 cricket world cup .

{'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'catches'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; catches }'}, 'player'], 'result': 'm kaif', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; catches } ; player }'}, 'm kaif'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; catches } ; player } ; m kaif } = true', 'tointer': 'select the row whose catches record of all rows is maximum . the player record of this row is m kaif .'}

eq { hop { argmax { all_rows ; catches } ; player } ; m kaif } = true

select the row whose catches record of all rows is maximum . the player record of this row is m kaif .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'catches_5': 5, 'player_6': 6, 'm kaif_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'catches_5': 'catches', 'player_6': 'player', 'm kaif_7': 'm kaif'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'catches_5': [0], 'player_6': [1], 'm kaif_7': [2]}

['catches', 'player', 'versus', 'venue', 'date']

[['4', 'm kaif', 'sri lanka', 'johannesburg', '10 - 03 - 2003'], ['3', 'v sehwag', 'netherlands', 'paarl', '12 - 02 - 2003'], ['3', 'lj burger', 'england', 'port elizabeth', '19 - 02 - 2003'], ['3', 'jp maher', 'netherlands', 'potchefstroom', '20 - 02 - 2003'], ['3', 'hh dippenaar', 'bangladesh', 'bloemfontein', '22 - 02 - 2003'], ['3', 'd mongia', 'namibia', 'pietermaritzburg', '23 - 02 - 2003'], ['3', 'v sehwag', 'england', 'durban', '26 - 02 - 2003'], ['3', 'af giles', 'australia', 'port elizabeth', '02 - 03 - 2003']]

united states house of representatives elections in washington , 2008

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections_in_Washington%2C_2008

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16185956-1.html.csv

aggregation

the average winning percentage of the candidates in washington districts , who ran in the 2008 united states house of representative elections was almost 66 % .

{'scope': 'all', 'col': '7', 'type': 'average', 'result': '66 %', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'results'], 'result': '66 %', 'ind': 0, 'tostr': 'avg { all_rows ; results }'}, '66 %'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; results } ; 66 % } = true', 'tointer': 'the average of the results record of all rows is 66 % .'}

round_eq { avg { all_rows ; results } ; 66 % } = true

the average of the results record of all rows is 66 % .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'results_4': 4, '66%_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'results_4': 'results', '66%_5': '66 %'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'results_4': [0], '66%_5': [1]}

['district', 'incumbent', 'party', 'elected', 'status', '2008 candidates', 'results']

[['washington 1', 'jay inslee', 'democrat', '1998', 'running', 'jay inslee ( d ) ( cw ) larry ishmael ( r ) ( cw )', '68 % 32 %'], ['washington 2', 'rick larsen', 'democrat', '2000', 'running', 'rick larsen ( d ) ( cw ) rick bart ( r ) ( cw )', '62 % 38 %'], ['washington 3', 'brian baird', 'democrat', '1998', 'running', 'brian baird ( d ) ( cw ) michael delavar ( r ) ( cw )', '64 % 36 %'], ['washington 4', 'doc hastings', 'republican', '1994', 'running', 'doc hastings ( r ) ( cw ) george fearing ( d ) ( cw )', '63 % 37 %'], ['washington 5', 'cathy mcmorris', 'republican', '2004', 'running', 'cathy mcmorris ( r ) ( cw ) mark mays ( d ) ( cw )', '65 % 35 %'], ['washington 6', 'norm dicks', 'democrat', '1976', 'running', 'norm dicks ( d ) ( cw ) doug cloud ( r ) ( cw )', '67 % 33 %'], ['washington 7', 'jim mcdermott', 'democrat', '1988', 'running', 'jim mcdermott ( d ) ( cw ) steve beren ( r ) ( cw )', '84 % 16 %'], ['washington 8', 'dave reichert', 'republican', '2004', 'running', 'dave reichert ( r ) ( cw ) darcy burner ( d ) ( cw )', '53 % 47 %'], ['washington 9', 'adam smith', 'democrat', '1996', 'running', 'adam smith ( d ) ( cw ) james postma ( r ) ( cw )', '65 % 35 %']]

carlos kirmayr

https://en.wikipedia.org/wiki/Carlos_Kirmayr

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17262467-1.html.csv

comparative

carlos kirmayr played a match in chile before he had a match in the us .

{'row_1': '1', 'row_2': '4', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'santiago , chile'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose championship record fuzzily matches to santiago , chile .', 'tostr': 'filter_eq { all_rows ; championship ; santiago , chile }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; championship ; santiago , chile } ; date }', 'tointer': 'select the rows whose championship record fuzzily matches to santiago , chile . take the date record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'forest hills , us'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose championship record fuzzily matches to forest hills , us .', 'tostr': 'filter_eq { all_rows ; championship ; forest hills , us }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; championship ; forest hills , us } ; date }', 'tointer': 'select the rows whose championship record fuzzily matches to forest hills , us . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; championship ; santiago , chile } ; date } ; hop { filter_eq { all_rows ; championship ; forest hills , us } ; date } } = true', 'tointer': 'select the rows whose championship record fuzzily matches to santiago , chile . take the date record of this row . select the rows whose championship record fuzzily matches to forest hills , us . take the date record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; championship ; santiago , chile } ; date } ; hop { filter_eq { all_rows ; championship ; forest hills , us } ; date } } = true

select the rows whose championship record fuzzily matches to santiago , chile . take the date record of this row . select the rows whose championship record fuzzily matches to forest hills , us . take the date record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'championship_7': 7, 'santiago , chile_8': 8, 'date_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'championship_11': 11, 'forest hills , us_12': 12, 'date_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'championship_7': 'championship', 'santiago , chile_8': 'santiago , chile', 'date_9': 'date', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'championship_11': 'championship', 'forest hills , us_12': 'forest hills , us', 'date_13': 'date'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'championship_7': [0], 'santiago , chile_8': [0], 'date_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'championship_11': [1], 'forest hills , us_12': [1], 'date_13': [3]}

['outcome', 'date', 'championship', 'surface', 'opponent in the final', 'score in the final']

[['runner - up', '1976', 'santiago , chile', 'clay', 'josé higueras', '7 - 5 , 4 - 6 , 4 - 6'], ['runner - up', '1979', 'cairo , egypt', 'clay', 'peter feigl', '5 - 7 , 6 - 3 , 1 - 6'], ['runner - up', '1980', 'bogotá , colombia', 'clay', 'dominique bedel', '4 - 6 , 6 - 7'], ['runner - up', '1981', 'forest hills , us', 'clay', 'eddie dibbs', '3 - 6 , 2 - 6'], ['runner - up', '1982', 'guarujá , brazil', 'clay', 'van winitsky', '3 - 6 , 3 - 6']]

northerly

https://en.wikipedia.org/wiki/Northerly

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1358608-2.html.csv

aggregation

for northerly the average weight from 2000 to 2001 was 55.75 .

{'scope': 'all', 'col': '7', 'type': 'average', 'result': '55.75', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'weight ( kg )'], 'result': '55.75', 'ind': 0, 'tostr': 'avg { all_rows ; weight ( kg ) }'}, '55.75'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; weight ( kg ) } ; 55.75 } = true', 'tointer': 'the average of the weight ( kg ) record of all rows is 55.75 .'}

round_eq { avg { all_rows ; weight ( kg ) } ; 55.75 } = true

the average of the weight ( kg ) record of all rows is 55.75 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'weight (kg)_4': 4, '55.75_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'weight (kg)_4': 'weight ( kg )', '55.75_5': '55.75'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'weight (kg)_4': [0], '55.75_5': [1]}

['result', 'date', 'race', 'venue', 'group', 'distance', 'weight ( kg )', 'jockey', 'winner / 2nd']

[['4th', '11 nov 2000', '3yo & up hcp restricted', 'ascot', 'na', '1400 m', '53.5', 'p carbery', '1st - great beau'], ['won', '30 nov 2000', '3yo & up hcp restricted', 'ascot', 'na', '1400 m', '56.5', 'p carbery', '2nd - echoes'], ['won', '23 dec 2000', 'r j peters stakes', 'ascot', 'g3', '1500 m', '52', 'p carbery', '2nd - special jester'], ['won', '30 dec 2000', 'railway stakes', 'ascot', 'g1', '1600 m', '51', 'd miller', '2nd - old comrade'], ['2nd', '27 jan 2001', 'australia day stakes', 'ascot', 'lr', '1200 m', '58', 'p carbery', '1st - exit lane'], ['won', '17 feb 2001', 'clyon cup', 'caulfield', 'g2', '1600 m', '58', 'g childs', '2nd - oval office'], ['3rd', '03 mar 2001', 'victoria cup', 'caulfield', 'na', '2024 m', '59', 'b prebble', '1st - greenstone charm'], ['won', '12 mar 2001', 'australia cup', 'flemington', 'g1', '2000 m', '58', 'g childs', '2nd - hit the roof']]

1944 vfl season

https://en.wikipedia.org/wiki/1944_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809142-18.html.csv

ordinal

during the 1944 vfl season , the game with the highest attendance was at princes park .

{'row': '3', 'col': '6', 'order': '1', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'princes park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'princes park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true', 'tointer': 'select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .'}

eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true

select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '1_6': 6, 'venue_7': 7, 'princes park_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '1_6': '1', 'venue_7': 'venue', 'princes park_8': 'princes park'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '1_6': [0], 'venue_7': [1], 'princes park_8': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['essendon', '17.24 ( 126 )', 'south melbourne', '6.8 ( 44 )', 'windy hill', '11000', '2 september 1944'], ['collingwood', '10.8 ( 68 )', 'richmond', '15.18 ( 108 )', 'victoria park', '14000', '2 september 1944'], ['carlton', '13.10 ( 88 )', 'footscray', '12.17 ( 89 )', 'princes park', '34000', '2 september 1944'], ['st kilda', '12.10 ( 82 )', 'north melbourne', '16.15 ( 111 )', 'junction oval', '7000', '2 september 1944'], ['melbourne', '14.27 ( 111 )', 'hawthorn', '6.13 ( 49 )', 'punt road oval', '4000', '2 september 1944'], ['geelong', '10.10 ( 70 )', 'fitzroy', '15.15 ( 105 )', 'kardinia park', '8000', '2 september 1944']]

2008 hamilton tiger - cats season

https://en.wikipedia.org/wiki/2008_Hamilton_Tiger-Cats_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16912076-4.html.csv

majority

the hamilton tiger-cats lost most of their games in the 2008 season .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'loss', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'loss'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to loss .', 'tostr': 'most_eq { all_rows ; result ; loss } = true'}

most_eq { all_rows ; result ; loss } = true

for the result records of all rows , most of them fuzzily match to loss .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'loss_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'loss_4': 'loss'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'loss_4': [0]}

['week', 'date', 'opponent', 'score', 'result', 'attendance', 'record']

[['1', 'june 26', 'montreal alouettes', '33 - 10', 'loss', '20589', '0 - 1'], ['2', 'july 3', 'toronto argonauts', '32 - 13', 'win', '30822', '1 - 1'], ['3', 'july 12', 'saskatchewan roughriders', '33 - 28', 'loss', '20874', '1 - 2'], ['4', 'july 17', 'calgary stampeders', '43 - 16', 'loss', '31116', '1 - 3'], ['5', 'july 24', 'edmonton eskimos', '19 - 13', 'loss', '21402', '1 - 4'], ['6', 'july 31', 'montreal alouettes', '40 - 33', 'loss', '20202', '1 - 5'], ['7', 'aug 7', 'toronto argonauts', '45 - 21', 'win', '19423', '2 - 5'], ['8', 'aug 14', 'winnipeg blue bombers', '37 - 24', 'loss', '25484', '2 - 6'], ['9', '-', '-', '-', '-', '-', ''], ['10', 'sept 1', 'toronto argonauts', '34 - 31', 'loss', '25911', '2 - 7'], ['11', 'sept 6', 'bc lions', '35 - 12', 'loss', '18723', '2 - 8'], ['12', 'sept 13', 'edmonton eskimos', '38 - 33', 'loss', '37500', '2 - 9'], ['13', 'sept 19', 'winnipeg blue bombers', '25 - 23', 'loss', '19102', '2 - 10'], ['14', 'sept 27', 'bc lions', '40 - 10', 'loss', '31161', '2 - 11'], ['15', 'oct 4', 'montreal alouettes', '44 - 36', 'win', '20423', '3 - 11'], ['16', 'oct 13', 'montreal alouettes', '42 - 11', 'loss', '20202', '3 - 12'], ['17', 'oct 19', 'saskatchewan roughriders', '30 - 29', 'loss', '30945', '3 - 13'], ['18', 'oct 24', 'calgary stampeders', '28 - 17', 'loss', '20614', '3 - 14'], ['19', 'nov 1', 'winnipeg blue bombers', '44 - 30', 'loss', '24595', '3 - 15']]

maneater ( film series )

https://en.wikipedia.org/wiki/Maneater_%28film_series%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19982699-1.html.csv

ordinal

for the maneater film series , the title with the 2nd to last television premiere date was behemoth .

{'row': '6', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'television premiere', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; television premiere ; 2 }'}, 'title'], 'result': 'behemoth', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; television premiere ; 2 } ; title }'}, 'behemoth'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; television premiere ; 2 } ; title } ; behemoth } = true', 'tointer': 'select the row whose television premiere record of all rows is 2nd maximum . the title record of this row is behemoth .'}

eq { hop { nth_argmax { all_rows ; television premiere ; 2 } ; title } ; behemoth } = true

select the row whose television premiere record of all rows is 2nd maximum . the title record of this row is behemoth .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'television premiere_5': 5, '2_6': 6, 'title_7': 7, 'behemoth_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'television premiere_5': 'television premiere', '2_6': '2', 'title_7': 'title', 'behemoth_8': 'behemoth'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'television premiere_5': [0], '2_6': [0], 'title_7': [1], 'behemoth_8': [2]}

['', 'title', 'maneater', 'television premiere', 'dvd release', 'writer', 'director', 'producer']

[['1', 'blood monkey', 'chimpanzees', 'january 27 , 2007', 'november 6 , 2007', 'george lavoo gary dauberman', 'robert young', 'charles salmon'], ['2', "in the spider 's web", 'venomous spiders', 'august 26 , 2007', 'november 6 , 2007', 'gary dauberman', 'terry windsor', 'charles salmon'], ['7', 'grizzly rage', 'grizzly bear', 'june 7 , 2007', 'may 6 , 2008', 'arne olsen', 'david decoteau', 'robert halmi , sr phyllis lain'], ['8', 'the hive', 'army ants', 'february 17 , 2008', 'august 5 , 2008', 'ts cook', 'peter manus', 'charles salmon robert halmi sr robert halmi jr'], ['17', 'sand serpents', 'prehistoric s worm', 'july 11 , 2009', 'november 3 , 2009', 'raul inglis', 'jeff renfroe', 'ric nish'], ['23', 'behemoth', 'behemoth', 'january 15 , 2011', 'april 5 , 2011', 'rachelle s howie', 'wd hogan', 'john prince'], ['24', 'ferocious planet', 'beasts from parallel dimension', 'april 9 , 2011', 'july 5 , 2011', 'douglas g davis', "billy o'brien", 'mary callery']]

miss mundo dominicana 2004

https://en.wikipedia.org/wiki/Miss_Mundo_Dominicana_2004

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21346767-3.html.csv

count

19 contestants participated in the miss mundo dominicana 2004 peagant .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '19', 'col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'contestant'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose contestant record is arbitrary .', 'tostr': 'filter_all { all_rows ; contestant }'}], 'result': '19', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; contestant } }', 'tointer': 'select the rows whose contestant record is arbitrary . the number of such rows is 19 .'}, '19'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; contestant } } ; 19 } = true', 'tointer': 'select the rows whose contestant record is arbitrary . the number of such rows is 19 .'}

eq { count { filter_all { all_rows ; contestant } } ; 19 } = true

select the rows whose contestant record is arbitrary . the number of such rows is 19 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'contestant_5': 5, '19_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'contestant_5': 'contestant', '19_6': '19'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'contestant_5': [0], '19_6': [2]}

['province , community', 'contestant', 'age', 'height', 'hometown', 'geographical regions']

[['azua', 'julissa alcantara de fiallo', '22', 'm ( ft 9in )', 'santo domingo', 'sur occidente'], ['barahona', 'desireé álvarez lama', '19', 'm ( ft 9\xa03⁄4 in )', 'santa cruz de barahona', 'sur occidente'], ['com dom en california', 'mónica angulo pucheaux', '18', 'm ( ft 9\xa01⁄4 in )', 'los angeles', 'exterior'], ['com dom en miami', 'onidys reynosa espinal', '21', 'm ( ft 8in )', 'miami', 'exterior'], ['com dom en nueva york', 'joslyn cabrera ruiz', '18', 'm ( ft 11\xa01⁄4 in )', 'new york', 'exterior'], ['distrito nacional', 'katherine germania almos rey', '24', 'm ( ft 10\xa01⁄2 in )', 'villa juana', 'sur oriente'], ['duarte', 'lissette abreu ynoa', '22', 'm ( ft 7\xa01⁄4 in )', 'constanza', 'cibao oriental'], ['independencia', 'nathalie venecia gutiérrez arias', '20', 'm ( ft 11\xa03⁄4 in )', 'santo domingo', 'sur occidente'], ['la altagracia', 'patrizia karina gagg jiménez', '20', 'm ( ft 11\xa01⁄4 in )', 'villa hermosa', 'sur oriente'], ['la romana', 'anna karina toledo espinoza', '22', 'm ( ft 6\xa01⁄4 in )', 'la romana', 'sur oriente'], ['la vega', 'cindy magdalena torrealba cruz', '17', 'm ( ft 10\xa01⁄2 in )', 'jarabacoa', 'cibao central'], ['maría trinidad sánchez', 'massiel javier cañizarez', '19', 'm ( ft 10\xa03⁄4 in )', 'cabrera', 'cibao oriental'], ['monseñor nouel', 'claudia julissa cruz rodríguez', '18', 'm ( ft 9in )', 'bonao', 'cibao central'], ['monte cristi', 'hareld ellien mossle casado', '17', 'm ( ft 7in )', 'santiago de los caballeros', 'cibao occidental'], ['puerto plata', 'wilma joana abreu nazario', '20', 'm ( ft 6\xa01⁄2 in )', 'santiago de los caballeros', 'cibao occidental'], ['salcedo', 'josefina de arias camacho', '24', 'm ( ft 5\xa01⁄4 in )', 'santo domingo', 'cibao central'], ['samaná', 'genevet nicol gutiérrez kourie', '18', 'm ( ft 7\xa03⁄4 in )', 'santa bárbara de samaná', 'cibao oriental'], ['san pedro de macorís', 'natascha forestieri de los santos', '23', 'm ( ft 0in )', 'san pedro de macorís', 'sur oriente'], ['santiago', 'cindy guerrero reynoso', '26', 'm ( ft 10in )', 'santiago de los caballeros', 'cibao occidental']]

2010 fedex cup playoffs

https://en.wikipedia.org/wiki/2010_FedEx_Cup_Playoffs

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28498999-4.html.csv

count

in the 2010 fedex cup playoffs , the united states had 4 players in the top 9 .

{'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '4', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; united states } } ; 4 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; country ; united states } } ; 4 } = true

select the rows whose country record fuzzily matches to united states . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], '4_7': [2]}

['', 'player', 'country', 'score', 'to par', 'winnings', 'after', 'before']

[['1', 'charley hoffman', 'united states', '64 + 67 + 69 + 62 = 262', '- 22', '1350000', '2', '59'], ['t2', 'jason day', 'australia', '63 + 67 + 66 + 71 = 267', '- 17', '560000', '4', '14'], ['t2', 'luke donald', 'england', '65 + 67 + 66 + 69 = 267', '- 17', '560000', '5', '17'], ['t2', 'geoff ogilvy', 'australia', '64 + 72 + 65 + 66 = 267', '- 17', '560000', '9', '52'], ['t5', 'tom gillis', 'united states', '67 + 71 + 65 + 65 = 268', '- 16', '273750', '48', '92'], ['t5', 'adam scott', 'australia', '67 + 69 + 65 + 67 = 268', '- 16', '273750', '15', '19'], ['t5', 'brandt snedeker', 'united states', '66 + 64 + 67 + 71 = 268', '- 16', '273750', '31', '53'], ['8', 'john senden', 'australia', '66 + 68 + 69 + 67 = 270', '- 14', '232500', '38', '64'], ['9', 'steve stricker', 'united states', '65 + 68 + 67 + 71 = 271', '- 13', '217500', '3', '2']]

2008 - 09 ue lleida season

https://en.wikipedia.org/wiki/2008%E2%80%9309_UE_Lleida_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19018191-5.html.csv

aggregation

the players in the 2008 - 09 ue lleida season scored a total number of 44 league goals .

{'scope': 'all', 'col': '8', 'type': 'sum', 'result': '44', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'total g'], 'result': '44', 'ind': 0, 'tostr': 'sum { all_rows ; total g }'}, '44'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; total g } ; 44 } = true', 'tointer': 'the sum of the total g record of all rows is 44 .'}

round_eq { sum { all_rows ; total g } ; 44 } = true

the sum of the total g record of all rows is 44 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'total g_4': 4, '44_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'total g_4': 'total g', '44_5': '44'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'total g_4': [0], '44_5': [1]}

['player', 'nat', 'pos', 'l apps', 'l g', 'c apps', 'total apps', 'total g']

[['gabernet', 'esp', 'mf', '15', '1', '0', '15', '1'], ['galán', 'esp', 'df', '32', '2', '0', '32', '2'], ['jerson', 'esp', 'df', '34', '1', '0', '34', '1'], ['urrea', 'esp', 'mf', '16', '0', '0', '16', '0'], ['dani marín', 'esp', 'df', '30', '1', '0', '30', '1'], ['campabadal', 'esp', 'mf', '32', '1', '0', '32', '1'], ['parra', 'esp', 'fw', '37', '4', '0', '37', '4'], ['figuerola', 'esp', 'mf', '25', '0', '0', '25', '0'], ['mikel álvaro', 'esp', 'mf', '36', '13', '0', '36', '13'], ['ermengol', 'esp', 'fw', '24', '0', '0', '24', '0'], ['miki', 'esp', 'mf', '36', '1', '0', '36', '1'], ['benet', 'esp', 'mf', '4', '0', '0', '4', '0'], ['moya', 'esp', 'df', '31', '5', '0', '31', '5'], ['david giménez', 'esp', 'mf', '36', '6', '0', '36', '6'], ['casado', 'esp', 'df', '27', '1', '0', '27', '1'], ['jaume', 'esp', 'mf', '35', '0', '0', '35', '0'], ['sellarés', 'esp', 'fw', '37', '8', '0', '37', '8']]

boston university terriers men 's ice hockey

https://en.wikipedia.org/wiki/Boston_University_Terriers_men%27s_ice_hockey

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12453414-5.html.csv

ordinal

for boston university terriers men 's ice hockey , the 2nd highest number of goals was by john cullen .

{'row': '1', 'col': '3', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'goals', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; goals ; 2 }'}, 'player'], 'result': 'john cullen', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; goals ; 2 } ; player }'}, 'john cullen'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; goals ; 2 } ; player } ; john cullen } = true', 'tointer': 'select the row whose goals record of all rows is 2nd maximum . the player record of this row is john cullen .'}

eq { hop { nth_argmax { all_rows ; goals ; 2 } ; player } ; john cullen } = true

select the row whose goals record of all rows is 2nd maximum . the player record of this row is john cullen .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'goals_5': 5, '2_6': 6, 'player_7': 7, 'john cullen_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'goals_5': 'goals', '2_6': '2', 'player_7': 'player', 'john cullen_8': 'john cullen'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'goals_5': [0], '2_6': [0], 'player_7': [1], 'john cullen_8': [2]}

['player', 'years', 'goals', 'assists', 'points']

[['john cullen', '1983 - 87', '98', '143', '241'], ['david sacco', '1989 - 93', '74', '143', '217'], ['chris drury', '1994 - 98', '113', '101', '214'], ['rick meagher', '1973 - 77', '90', '120', '210'], ['mike eruzione', '1973 - 77', '92', '116', '208'], ['shawn mceachern', '1988 - 91', '79', '107', '186'], ['david tomlinson', '1987 - 91', '77', '102', '179'], ['mark fidler', '1977 - 81', '77', '101', '178'], ['mike kelfer', '1985 - 89', '83', '89', '172'], ['mike hyndman', '1967 - 70', '52', '119', '171']]

rear enz

https://en.wikipedia.org/wiki/Rear_Enz

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11221498-1.html.csv

majority

tim finn was the author or co author for a majority of the titles .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 't finn', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'author ( s )', 't finn'], 'result': True, 'ind': 0, 'tointer': 'for the author ( s ) records of all rows , most of them fuzzily match to t finn .', 'tostr': 'most_eq { all_rows ; author ( s ) ; t finn } = true'}

most_eq { all_rows ; author ( s ) ; t finn } = true

for the author ( s ) records of all rows , most of them fuzzily match to t finn .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'author (s)_3': 3, 't finn_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'author (s)_3': 'author ( s )', 't finn_4': 't finn'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'author (s)_3': [0], 't finn_4': [0]}

['track', 'title', 'author ( s )', 'recorded', 'length']

[['1', 'firedrill', 'tim finn , neil finn , eddie rayner', 'may 1982', '3:55'], ['2', 'your inspiration', 'n finn', 'april 1984', '3:49'], ['3', 'parasite', 't finn', 'october 1983', '3:37'], ['4', 'next exit', 't finn', 'march 1983', '3:40'], ['5', 'over drive', 'rayner', 'september 1984', '3:43'], ['6', 'serge', 'n finn', 'aav studios , 1984', '3:35'], ['7', 'in the wars', 't finn', 'november 1980', '3:06'], ['8', 'love & success', 'n finn', 'april 1984', '3:04'], ['9', 'big heart', 't finn', 'april 1984', '3:42'], ['10', 'mr catalyst', 'rayner , t finn', 'april 1984', '3:38'], ['11', 'remember when', 't finn', 'march 1983', '3:15']]

2007 - 08 uefa champions league knockout stage

https://en.wikipedia.org/wiki/2007%E2%80%9308_UEFA_Champions_League_knockout_stage

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14679997-3.html.csv

unique

for the 2007 - 08 uefa champions league knockout stage , when the first leg is 0-0 , the only time that team 2 is milan , is when team 1 is arsenal .

{'scope': 'subset', 'row': '6', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'milan', 'subset': {'col': '4', 'criterion': 'equal', 'value': '0-0'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '0-0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; 1st leg ; 0-0 }', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 .'}, 'team 2', 'milan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 . among these rows , select the rows whose team 2 record fuzzily matches to milan .', 'tostr': 'filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } }', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 . among these rows , select the rows whose team 2 record fuzzily matches to milan . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '0-0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; 1st leg ; 0-0 }', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 .'}, 'team 2', 'milan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 . among these rows , select the rows whose team 2 record fuzzily matches to milan .', 'tostr': 'filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan }'}, 'team 1'], 'result': 'arsenal', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } ; team 1 }'}, 'arsenal'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } ; team 1 } ; arsenal }', 'tointer': 'the team 1 record of this unqiue row is arsenal .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } } ; eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } ; team 1 } ; arsenal } } = true', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 0-0 . among these rows , select the rows whose team 2 record fuzzily matches to milan . there is only one such row in the table . the team 1 record of this unqiue row is arsenal .'}

and { only { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } } ; eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } ; team 1 } ; arsenal } } = true

select the rows whose 1st leg record fuzzily matches to 0-0 . among these rows , select the rows whose team 2 record fuzzily matches to milan . there is only one such row in the table . the team 1 record of this unqiue row is arsenal .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, '1st leg_8': 8, '0-0_9': 9, 'team 2_10': 10, 'milan_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'team 1_12': 12, 'arsenal_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', '1st leg_8': '1st leg', '0-0_9': '0-0', 'team 2_10': 'team 2', 'milan_11': 'milan', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'team 1_12': 'team 1', 'arsenal_13': 'arsenal'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], '1st leg_8': [0], '0-0_9': [0], 'team 2_10': [1], 'milan_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'team 1_12': [3], 'arsenal_13': [4]}

['team 1', 'agg', 'team 2', '1st leg', '2nd leg']

[['celtic', '2 - 4', 'barcelona', '2 - 3', '0 - 1'], ['lyon', '1 - 2', 'manchester united', '1 - 1', '0 - 1'], ['schalke 04', '1 - 1 ( 4 - 1 p )', 'porto', '1 - 0', '0 - 1 ( aet )'], ['liverpool', '3 - 0', 'internazionale', '2 - 0', '1 - 0'], ['roma', '4 - 2', 'real madrid', '2 - 1', '2 - 1'], ['arsenal', '2 - 0', 'milan', '0 - 0', '2 - 0'], ['olympiacos', '0 - 3', 'chelsea', '0 - 0', '0 - 3'], ['fenerbahçe', '5 - 5 ( 3 - 2 p )', 'sevilla', '3 - 2', '2 - 3 ( aet )']]

jeff andretti

https://en.wikipedia.org/wiki/Jeff_Andretti

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1617211-2.html.csv

comparative

jeff andretti 's finish in 1991 was better than his finish in 1993 .

{'row_1': '2', 'row_2': '4', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1991'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1991 .', 'tostr': 'filter_eq { all_rows ; year ; 1991 }'}, 'finish'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1991 } ; finish }', 'tointer': 'select the rows whose year record fuzzily matches to 1991 . take the finish record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1993'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1993 .', 'tostr': 'filter_eq { all_rows ; year ; 1993 }'}, 'finish'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1993 } ; finish }', 'tointer': 'select the rows whose year record fuzzily matches to 1993 . take the finish record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; year ; 1991 } ; finish } ; hop { filter_eq { all_rows ; year ; 1993 } ; finish } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1991 . take the finish record of this row . select the rows whose year record fuzzily matches to 1993 . take the finish record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; year ; 1991 } ; finish } ; hop { filter_eq { all_rows ; year ; 1993 } ; finish } } = true

select the rows whose year record fuzzily matches to 1991 . take the finish record of this row . select the rows whose year record fuzzily matches to 1993 . take the finish record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1991_8': 8, 'finish_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1993_12': 12, 'finish_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1991_8': '1991', 'finish_9': 'finish', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1993_12': '1993', 'finish_13': 'finish'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1991_8': [0], 'finish_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1993_12': [1], 'finish_13': [3]}

['year', 'chassis', 'engine', 'start', 'finish']

[['1990', 'lola', 'cosworth', 'failed to qualify', 'failed to qualify'], ['1991', 'lola', 'cosworth', '11th', '15th'], ['1992', 'lola', 'chevrolet', '20th', '18th'], ['1993', 'lola', 'buick', '16th', '29th'], ['1994', 'lola', 'buick', 'failed to qualify', 'failed to qualify']]

hamburg state election , 2004

https://en.wikipedia.org/wiki/Hamburg_state_election%2C_2004

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1265169-2.html.csv

majority

most of the parties which participated in the 2004 hamburg state election had 0.0 seat percentage .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0.0', 'subset': None}

{'func': 'most_eq', 'args': ['all_rows', 'seat percentage', '0.0'], 'result': True, 'ind': 0, 'tointer': 'for the seat percentage records of all rows , most of them are equal to 0.0 .', 'tostr': 'most_eq { all_rows ; seat percentage ; 0.0 } = true'}

most_eq { all_rows ; seat percentage ; 0.0 } = true

for the seat percentage records of all rows , most of them are equal to 0.0 .

1

1

{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'seat percentage_3': 3, '0.0_4': 4}

{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'seat percentage_3': 'seat percentage', '0.0_4': '0.0'}

{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'seat percentage_3': [0], '0.0_4': [0]}

['party', 'party list votes', 'vote percentage', 'total seats', 'seat percentage']

[['christian democratic union ( cdu )', '389170', '47.2 % ( + 21.0 )', '63 ( + 30 )', '52.1 %'], ['social democratic party ( spd )', '251441', '30.5 % ( - 6.0 )', '41 ( - 5 )', '33.9 %'], ['green - alternative list ( gal )', '101227', '12.3 % ( + 3.7 )', '17 ( + 6 )', '14.0 %'], ['pro deutsche mitte ( pro dm / schill )', '25763', '3.1 % ( + 2.9 )', '0 ( + 0 )', '0.0 %'], ['free democratic party ( fdp )', '23373', '2.8 % ( - 2.2 )', '0 ( - 6 )', '0.0 %'], ['rainbow - for a new left ( regenbogen )', '9221', '1.1 % ( - 0.6 )', '0 ( + 0 )', '0.0 %'], ['grey panthers party of germany ( graue )', '8862', '1.1 % ( + 0.8 )', '0 ( + 0 )', '0.0 %'], ['law and order offensive party ( offensive )', '3041', '0.4 % ( - 19.1 )', '0 ( - 25 )', '0.0 %'], ['all others', '12030', '1.5 % ( - 0.5 )', '0', '0.0 %'], ['totals', '824128', '100.0 %', '121', '100.0 %']]

advanced television systems committee standards

https://en.wikipedia.org/wiki/Advanced_Television_Systems_Committee_standards

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-272313-1.html.csv

majority

of the listed advanced television systems committee standards the majority use a 1:1 pixel aspect ratio .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1:1', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'pixel aspect ratio', '1:1'], 'result': True, 'ind': 0, 'tointer': 'for the pixel aspect ratio records of all rows , most of them fuzzily match to 1:1 .', 'tostr': 'most_eq { all_rows ; pixel aspect ratio ; 1:1 } = true'}

most_eq { all_rows ; pixel aspect ratio ; 1:1 } = true

for the pixel aspect ratio records of all rows , most of them fuzzily match to 1:1 .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'pixel aspect ratio_3': 3, '1:1_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'pixel aspect ratio_3': 'pixel aspect ratio', '1:1_4': '1:1'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'pixel aspect ratio_3': [0], '1:1_4': [0]}

['vertical', 'horizontal', 'aspect ratio', 'pixel aspect ratio', 'scanning', 'frame rate ( hz )']

[['1080', '1920', '16:9', '1:1', 'progressive', '23.976 24 29.97 30'], ['1080', '1920', '16:9', '1:1', 'interlaced', '29.97 ( 59.94 fields / s ) 30 ( 60 fields / s )'], ['720', '1280', '16:9', '1:1', 'progressive', '23.976 24 29.97 30 59.94 60'], ['480', '704', '4:3 or 16:9', 'smpte 259 m', 'progressive', '23.976 24 29.97 30 59.94 60'], ['480', '704', '4:3 or 16:9', 'smpte 259 m', 'interlaced', '29.97 ( 59.94 fields / s ) 30 ( 60 fields / s )'], ['480', '640', '4:3', '1:1', 'progressive', '23.976 24 29.97 30 59.94 60']]

c - class destroyer ( 1943 )

https://en.wikipedia.org/wiki/C-class_destroyer_%281943%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1206583-1.html.csv

count

yarrow , scotstoun was the builder for two different c - class destroyers .

{'scope': 'all', 'criterion': 'equal', 'value': 'yarrow , scotstoun', 'result': '2', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'builder', 'yarrow , scotstoun'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose builder record fuzzily matches to yarrow , scotstoun .', 'tostr': 'filter_eq { all_rows ; builder ; yarrow , scotstoun }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; builder ; yarrow , scotstoun } }', 'tointer': 'select the rows whose builder record fuzzily matches to yarrow , scotstoun . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; builder ; yarrow , scotstoun } } ; 2 } = true', 'tointer': 'select the rows whose builder record fuzzily matches to yarrow , scotstoun . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; builder ; yarrow , scotstoun } } ; 2 } = true

select the rows whose builder record fuzzily matches to yarrow , scotstoun . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'builder_5': 5, 'yarrow , scotstoun_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'builder_5': 'builder', 'yarrow , scotstoun_6': 'yarrow , scotstoun', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'builder_5': [0], 'yarrow , scotstoun_6': [0], '2_7': [2]}

['name', 'pennant', 'builder', 'laid down', 'launched', 'commissioned']

[['caprice ( ex - swallow )', 'r01 later d01', 'yarrow , scotstoun', '24 september 1942', '16 september 1943', '5 april 1944'], ['cassandra ( ex - tourmaline )', 'r62 later d10', 'yarrow , scotstoun', '30 january 1943', '29 november 1943', '28 july 1944'], ['caesar ( ex - ranger )', 'r07 later d07', 'john brown , clydebank', '3 april 1943', '14 february 1944', '5 october 1944'], ['cavendish ( ex - sibyl )', 'r15 later d15', 'john brown , clydebank', '19 may 1943', '12 april 1944', '13 december 1944'], ['cambrian ( ex - spitfire )', 'r85 later d85', 'scotts , greenock', '14 august 1942', '10 december 1943', '17 july 1944 by john brown'], ['carron ( ex - strenuous )', 'r30 later d30', 'scotts , greenock', '26 november 1942', '28 march 1944', '6 november 1944'], ['cavalier ( ex - pellew )', 'r73 later d73', 'white , cowes', '28 february 1943', '7 april 1944', '22 november 1944'], ['carysfort ( ex - pique )', 'r25 later d25', 'white , cowes', '12 may 1943', '25 july 1944', '20 february 1945']]

tacoma public schools

https://en.wikipedia.org/wiki/Tacoma_Public_Schools

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1414702-3.html.csv

superlative

in the tacoma public schools , the high school with the highest enrollment is mount tahoma .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'high school'], 'result': 'mount tahoma', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; high school }'}, 'mount tahoma'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; high school } ; mount tahoma } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the high school record of this row is mount tahoma .'}

eq { hop { argmax { all_rows ; enrollment } ; high school } ; mount tahoma } = true

select the row whose enrollment record of all rows is maximum . the high school record of this row is mount tahoma .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'high school_6': 6, 'mount tahoma_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'high school_6': 'high school', 'mount tahoma_7': 'mount tahoma'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'high school_6': [1], 'mount tahoma_7': [2]}

['high school', 'type', 'established', 'enrollment', 'mascot', 'wiaa classification', 'notes']

[['henry foss', 'comprehensive', '1973', '1298', 'falcons', '3a', 'located in central tacoma'], ['lincoln', 'comprehensive', '1913', '1618', 'abes', '3a', 'located in east tacoma'], ['mount tahoma', 'comprehensive', '1961', '1865', 'thunderbirds', '3a', 'located in south tacoma'], ['oakland alternative', 'alternative', '1988', '106', 'eagles', 'n / a', 'located in central tacoma'], ['tacoma school of the arts', 'magnet', '2001', '500', 'n / a', 'n / a', 'located in downtown tacoma']]

list of highways in webb county , texas

https://en.wikipedia.org/wiki/List_of_highways_in_Webb_County%2C_Texas

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11336756-5.html.csv

ordinal

in the list of highways in webb county , texas , aguilares , texas us 59 has the highest route name number among those whose junction is sh 359 us 59 .

{'scope': 'subset', 'row': '4', 'col': '1', 'order': '1', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'sh 359 us 59'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'junctions', 'sh 359 us 59'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; junctions ; sh 359 us 59 }', 'tointer': 'select the rows whose junctions record fuzzily matches to sh 359 us 59 .'}, 'route name', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; route name ; 1 }'}, 'termini'], 'result': 'aguilares , texas us 59', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; route name ; 1 } ; termini }'}, 'aguilares , texas us 59'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; route name ; 1 } ; termini } ; aguilares , texas us 59 } = true', 'tointer': 'select the rows whose junctions record fuzzily matches to sh 359 us 59 . select the row whose route name record of these rows is 1st maximum . the termini record of this row is aguilares , texas us 59 .'}

eq { hop { nth_argmax { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; route name ; 1 } ; termini } ; aguilares , texas us 59 } = true

select the rows whose junctions record fuzzily matches to sh 359 us 59 . select the row whose route name record of these rows is 1st maximum . the termini record of this row is aguilares , texas us 59 .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'junctions_6': 6, 'sh 359 us 59_7': 7, 'route name_8': 8, '1_9': 9, 'termini_10': 10, 'aguilares , texas us 59_11': 11}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'junctions_6': 'junctions', 'sh 359 us 59_7': 'sh 359 us 59', 'route name_8': 'route name', '1_9': '1', 'termini_10': 'termini', 'aguilares , texas us 59_11': 'aguilares , texas us 59'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'junctions_6': [0], 'sh 359 us 59_7': [0], 'route name_8': [1], '1_9': [1], 'termini_10': [2], 'aguilares , texas us 59_11': [3]}

['route name', 'direction', 'termini', 'junctions', 'length', 'population area']

[['fm 649', 'south north', 'zapata county sh 359', 'sh 359', '-', 'mirando city'], ['fm 1472', 'south north', 'i - 35 a point miles ( km ) northwest of sh 255', 'i - 35 fm 3338 sh 255', '-', 'laredo'], ['fm 2050', 'south north', 'bruni , texas us 59', 'sh 359 us 59', '-', 'bruni'], ['fm 2895', 'south north', 'aguilares , texas us 59', 'sh 359 us 59', '-', 'aguilares'], ['fm 3338', 'south north', 'fm 1472 sh 255', 'ur 1472 sh 255', '-', 'laredo ranchos penitas west']]

ottoman - persian wars

https://en.wikipedia.org/wiki/Ottoman%E2%80%93Persian_Wars

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24706337-1.html.csv

count

two wars between the ottoman and persian empires ended in stalemate .

{'scope': 'all', 'criterion': 'equal', 'value': 'stalemate', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'victor', 'stalemate'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose victor record fuzzily matches to stalemate .', 'tostr': 'filter_eq { all_rows ; victor ; stalemate }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; victor ; stalemate } }', 'tointer': 'select the rows whose victor record fuzzily matches to stalemate . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; victor ; stalemate } } ; 2 } = true', 'tointer': 'select the rows whose victor record fuzzily matches to stalemate . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; victor ; stalemate } } ; 2 } = true

select the rows whose victor record fuzzily matches to stalemate . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'victor_5': 5, 'stalemate_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'victor_5': 'victor', 'stalemate_6': 'stalemate', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'victor_5': [0], 'stalemate_6': [0], '2_7': [2]}

['name of the war', 'ottoman sultan', 'persian shah', 'treaty at the end of the war', 'victor']

[['battle of chaldiran ( 1514 )', 'selim i', 'ismail i', 'none', 'the ottoman empire'], ['war of 1532 - 1555', 'suleiman i', 'tahmasp i', 'treaty of amasya ( 1555 )', 'the ottoman empire'], ['war of 1578 - 1590', 'murad iii', 'abbas i', 'treaty of constantinople ( 1590 )', 'the ottoman empire'], ['war of 1603 - 1618 , second stage', 'ahmed i , mustafa i , osman ii', 'abbas i', 'treaty of serav ( 1618 )', 'the persian empire'], ['war of 1623 - 1639', 'murad iv', 'abbas i , safi', 'treaty of zuhab ( 1639 )', 'the ottoman empire'], ['war of 1722 - 1727', 'ahmed iii', 'mahmud hotaki , ashraf hotaki', 'treaty of hamedan ( 1727 )', 'stalemate'], ['war of 1730 - 1736 , first stage', 'ahmed iii , mahmud i', 'nader shah', 'treaty of ahmet pasha ( 1732 )', 'the persian empire'], ['war of 1730 - 1736 , second stage', 'mahmud i', 'abbas iii , nader shah', 'treaty of constantinople ( 1736 )', 'the persian empire'], ['war of 1743 - 1746', 'mahmud i', 'nader shah', 'treaty of kerden ( 1746 )', 'stalemate']]

wisconsin badgers women 's basketball

https://en.wikipedia.org/wiki/Wisconsin_Badgers_women%27s_basketball

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16796096-2.html.csv

comparative

katie voigt scored more total points in wisconsin women 's basketball than janese banks did .

{'row_1': '8', 'row_2': '10', 'col': '1', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'katie voigt'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to katie voigt .', 'tostr': 'filter_eq { all_rows ; name ; katie voigt }'}, 'total points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; katie voigt } ; total points }', 'tointer': 'select the rows whose name record fuzzily matches to katie voigt . take the total points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'janese banks'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to janese banks .', 'tostr': 'filter_eq { all_rows ; name ; janese banks }'}, 'total points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; janese banks } ; total points }', 'tointer': 'select the rows whose name record fuzzily matches to janese banks . take the total points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; katie voigt } ; total points } ; hop { filter_eq { all_rows ; name ; janese banks } ; total points } } = true', 'tointer': 'select the rows whose name record fuzzily matches to katie voigt . take the total points record of this row . select the rows whose name record fuzzily matches to janese banks . take the total points record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; name ; katie voigt } ; total points } ; hop { filter_eq { all_rows ; name ; janese banks } ; total points } } = true

select the rows whose name record fuzzily matches to katie voigt . take the total points record of this row . select the rows whose name record fuzzily matches to janese banks . take the total points record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'katie voigt_8': 8, 'total points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'janese banks_12': 12, 'total points_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'katie voigt_8': 'katie voigt', 'total points_9': 'total points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'janese banks_12': 'janese banks', 'total points_13': 'total points'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'katie voigt_8': [0], 'total points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'janese banks_12': [1], 'total points_13': [3]}

['total points', 'name', 'career games', 'position', 'years played', 'scored 1500 points', 'date and opponent']

[['2312', 'jolene anderson', '123 games', 'g', '2004 - 2008', 'jr / 85th game', '2 / 17 / 07 vs purdue'], ['1994', 'barb franke', '114 games', 'f / c', '1991 - 1996', 'sr / 90th game', '12 / 7 / 95 vs western illinois'], ['1915', 'jessie stomski', '123 games', 'f', '1998 - 2002', 'sr / 100th game', '12 / 11 / 01 vs uw - milwaukee'], ['1901', 'robin threatt', '114 games', 'g', '1988 - 1993', 'sr / 92nd game', '12 / 15 / 92 vs uw - milwaukee'], ['1879', 'theresa huff', '118 games', 'f / c', '1979 - 1983', 'sr / 97th game', '12 / 22 / 82 vs loyola - chicago'], ['1857', 'latonya sims', '124 games', 'f / g', '1997 - 2001', 'sr / 99th game', '11 / 22 / 00 vs notre dame'], ['1662', 'tamara moore', '124 games', 'g', '1998 - 2002', 'sr / 114th game', '1 / 30 / 02 vs iowa'], ['1576', 'katie voigt', '116 games', 'g', '1993 - 1998', 'sr / 112th game', '2 / 20 / 98 vs illinois'], ['1543', 'ann klapperich', '113 games', 'f', '1994 - 1998', 'sr / 110th game', '2 / 22 / 98 vs penn state'], ['1512', 'janese banks', '118 games', 'g', '2004 - 2008', 'sr / 116th game', '3 / 2 / 08 vs iowa']]

kurt maschler award

https://en.wikipedia.org/wiki/Kurt_Maschler_Award

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15641996-1.html.csv

ordinal

anthony browne was the second author to win the kurt maschler award .

{'row': '2', 'col': '1', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 2 }'}, 'author'], 'result': 'anthony browne', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 2 } ; author }'}, 'anthony browne'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 2 } ; author } ; anthony browne } = true', 'tointer': 'select the row whose year record of all rows is 2nd minimum . the author record of this row is anthony browne .'}

eq { hop { nth_argmin { all_rows ; year ; 2 } ; author } ; anthony browne } = true

select the row whose year record of all rows is 2nd minimum . the author record of this row is anthony browne .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '2_6': 6, 'author_7': 7, 'anthony browne_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', '2_6': '2', 'author_7': 'author', 'anthony browne_8': 'anthony browne'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '2_6': [0], 'author_7': [1], 'anthony browne_8': [2]}

['year', 'author', 'illustrator', 'title', 'publisher']

[['1982', 'angela carter ( ed and translator )', 'michael foreman', 'sleeping beauty and other favourite fairy tales', 'v gollancz'], ['1983', 'anthony browne', 'browne', 'gorilla', 'julia macrae'], ['1984', 'john burningham', 'burningham', 'granpa', 'j cape'], ['1985', 'ted hughes ( 1968 )', 'andrew davidson', 'the iron man', 'faber'], ['1986', 'allan ahlberg', 'janet ahlberg', 'the jolly postman', 'heinemann'], ['1987', 'charles causley', 'charles keeping', 'jack the treacle eater', 'macmillan'], ['1988', 'lewis carroll ( 1865 )', 'anthony browne', "alice 's adventures in wonderland", 'julia macrae'], ['1989', 'martin waddell', 'barbara firth', 'the park in the dark', 'walker'], ['1990', 'quentin blake', 'blake', 'all join in', 'j cape'], ['1991', 'colin mcnaughton', 'mcnaughton', "have you seen who 's just moved in next door to us", 'walker'], ['1992', 'raymond briggs', 'briggs', 'the man', 'julia macrae'], ['1993', 'karen wallace', 'mike bostock', 'think of an eel', 'walker'], ['1994', 'trish cooke', 'helen oxenbury', 'so much', 'walker'], ['1995', 'kathy henderson', 'patrick benson', 'the little boat', 'walker'], ['1996', 'babette cole', 'cole', 'drop dead', 'j cape'], ['1997', 'william mayne', 'jonathan heale', 'lady muck', 'heinemann'], ['1998', 'anthony browne', 'browne', 'voices in the park', 'doubleday'], ['1999', 'lewis carroll ( 1865 )', 'helen oxenbury', "alice 's adventures in wonderland", 'walker']]

daigakkō

https://en.wikipedia.org/wiki/Daigakk%C5%8D

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11390711-1.html.csv

ordinal

the japan coast guard academy was the second earliest founded daigakkō .

{'row': '1', 'col': '6', 'order': '2', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'foundation', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; foundation ; 2 }'}, 'english name'], 'result': 'japan coast guard academy', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; foundation ; 2 } ; english name }'}, 'japan coast guard academy'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; english name } ; japan coast guard academy } = true', 'tointer': 'select the row whose foundation record of all rows is 2nd minimum . the english name record of this row is japan coast guard academy .'}

eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; english name } ; japan coast guard academy } = true

select the row whose foundation record of all rows is 2nd minimum . the english name record of this row is japan coast guard academy .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'foundation_5': 5, '2_6': 6, 'english name_7': 7, 'japan coast guard academy_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'foundation_5': 'foundation', '2_6': '2', 'english name_7': 'english name', 'japan coast guard academy_8': 'japan coast guard academy'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'foundation_5': [0], '2_6': [0], 'english name_7': [1], 'japan coast guard academy_8': [2]}

['english name', 'japanese orthography', 'pronouciation', 'abbreviation', 'provider ( national government )', 'foundation']

[['japan coast guard academy', '海上保安大学校', 'kaijō hoan daigakkō', 'jcga', 'japan coast guard', '1951'], ['national college of nursing ( ko )', '国立看護大学校', 'kokuritsu kango daigakkō', 'ncn', 'national center for global health and medicine', '2001'], ['national defense academy of japan', '防衛大学校', 'bōei daigakkō', 'nda bōei - dai ( 防衛大 )', 'ministry of defense', '1952'], ['national defense medical college', '防衛医科大学校', 'bōei ika daigakkō', 'ndmc', 'ministry of defense', '1973'], ['meteorological college', '気象大学校', 'kishō daigakkō', 'mc ki - dai , kidaikō', 'japan meteorological agency', '1922']]

list of game of the year awards

https://en.wikipedia.org/wiki/List_of_Game_of_the_Year_awards

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1851722-36.html.csv

count

five of the game of the year awards went to games that can be played on the gamecube platform .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'gamecube', 'result': '5', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'platform ( s )', 'gamecube'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube .', 'tostr': 'filter_eq { all_rows ; platform ( s ) ; gamecube }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; platform ( s ) ; gamecube } }', 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; 5 } = true', 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to gamecube . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; 5 } = true

select the rows whose platform ( s ) record fuzzily matches to gamecube . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'platform (s)_5': 5, 'gamecube_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'platform (s)_5': 'platform ( s )', 'gamecube_6': 'gamecube', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'platform (s)_5': [0], 'gamecube_6': [0], '5_7': [2]}

['year', 'game', 'genre', 'platform ( s )', 'developer ( s )']

[['2001', 'super smash bros melee', 'fighting', 'gamecube', 'hal laboratory , inc'], ['2002', 'metroid prime', '( first - person ) action - adventure', 'gamecube', 'retro studios , nintendo'], ['2003', 'the legend of zelda : wind waker', 'action - adventure', 'gamecube', 'nintendo ead software development group no 3'], ['2004', 'halo 2', '( first - person ) shooter', 'xbox', 'bungie'], ['2005', 'resident evil 4', 'survival horror : ( third - person ) shooter', 'gamecube', 'capcom production studio 4'], ['2006', 'the legend of zelda : twilight princess', 'action - adventure', 'wii , gamecube', 'nintendo ead software development group no 3'], ['2007', 'super mario galaxy', 'platformer', 'wii', 'nintendo ead tokyo development group'], ['2008', 'metal gear solid 4 : guns of the patriots', 'stealth action', 'playstation 3', 'kojima productions'], ['2009', 'uncharted 2 : among thieves', 'action - adventure : ( third - person ) shooter', 'playstation 3', 'naughty dog'], ['2010', 'mass effect 2', 'action rpg : ( third - person ) shooter', 'xbox 360 , windows , playstation 3', 'bioware'], ['2011', 'the legend of zelda : skyward sword', 'action - adventure', 'wii', 'nintendo ead , monolith soft'], ['2012', 'borderlands 2', 'action rpg / first - person shooter', 'xbox 360 , playstation 3 , windows', 'gearbox software']]

racquetball at the world games

https://en.wikipedia.org/wiki/Racquetball_at_the_World_Games

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16142354-5.html.csv

ordinal

mexico won the second most gold medals in racquetball at the world games .

{'row': '2', 'col': '3', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'gold', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; gold ; 2 }'}, 'nation'], 'result': 'mexico', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; gold ; 2 } ; nation }'}, 'mexico'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; mexico } = true', 'tointer': 'select the row whose gold record of all rows is 2nd maximum . the nation record of this row is mexico .'}

eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; mexico } = true

select the row whose gold record of all rows is 2nd maximum . the nation record of this row is mexico .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'gold_5': 5, '2_6': 6, 'nation_7': 7, 'mexico_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'gold_5': 'gold', '2_6': '2', 'nation_7': 'nation', 'mexico_8': 'mexico'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], '2_6': [0], 'nation_7': [1], 'mexico_8': [2]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'united states', '9', '6', '4', '19'], ['2', 'mexico', '3', '2', '2', '7'], ['3', 'canada', '0', '2', '4', '6'], ['5', 'netherlands', '0', '1', '1', '2'], ['4', 'colombia', '0', '1', '0', '1'], ['5', 'chile', '0', '0', '1', '1'], ['total', 'total', '12', '12', '12', '36']]

1958 toronto argonauts season

https://en.wikipedia.org/wiki/1958_Toronto_Argonauts_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24138601-2.html.csv

superlative

during week 11 of the 1958 toronto agronauts season is when they had their highest attendance .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '13', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'week'], 'result': '11', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; week }'}, '11'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; week } ; 11 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the week record of this row is 11 .'}

eq { hop { argmax { all_rows ; attendance } ; week } ; 11 } = true

select the row whose attendance record of all rows is maximum . the week record of this row is 11 .

3

3

{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'week_6': 6, '11_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'week_6': 'week', '11_7': '11'}

{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'week_6': [1], '11_7': [2]}

['week', 'date', 'opponent', 'location', 'final score', 'attendance', 'record']

[['1', 'august 19', 'rough riders', 'landsdowne park', 'l 44 - 7', '18470', '0 - 1 - 0'], ['1', 'august 22', 'alouettes', 'varsity stadium', 'w 15 - 14', '19492', '1 - 1 - 0'], ['2', 'september 1', 'tiger - cats', 'civic stadium', 'l 31 - 24', '20946', '1 - 2 - 0'], ['3', 'september 5', 'tiger - cats', 'varsity stadium', 'l 26 - 17', '26781', '1 - 3 - 0'], ['4', 'september 13', 'alouettes', 'molson stadium', 'l 24 - 21', '22620', '1 - 4 - 0'], ['5', 'september 20', 'rough riders', 'varsity stadium', 'l 17 - 14', '20166', '1 - 5 - 0'], ['6', 'september 27', 'rough riders', 'landsdowne park', 'l 28 - 4', '18500', '1 - 6 - 0'], ['7', 'october 4', 'alouettes', 'varsity stadium', 'l 14 - 10', '16424', '1 - 7 - 0'], ['8', 'october 11', 'tiger - cats', 'civic stadium', 'l 28 - 15', '16583', '1 - 8 - 0'], ['8', 'october 13', 'tiger - cats', 'varsity stadium', 'w 37 - 0', '16583', '2 - 8 - 0'], ['9', 'october 18', 'rough riders', 'landsdowne park', 'w 41 - 0', '14313', '3 - 8 - 0'], ['10', 'october 25', 'rough riders', 'varsity stadium', 'w 42 - 24', '23334', '4 - 8 - 0'], ['11', 'november 1', 'alouettes', 'varsity stadium', 'l 44 - 7', '26813', '4 - 9 - 0']]

1965 belgian grand prix

https://en.wikipedia.org/wiki/1965_Belgian_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1122334-1.html.csv

ordinal

in the 1965 belgian grand prix , the driver with the third fastest time driving a car constructed by brm was lucien bianchi .

{'scope': 'subset', 'row': '12', 'col': '4', 'order': '3', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'brm'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'brm'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; brm }', 'tointer': 'select the rows whose constructor record fuzzily matches to brm .'}, 'time / retired', '3'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 }'}, 'driver'], 'result': 'lucien bianchi', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver }'}, 'lucien bianchi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver } ; lucien bianchi } = true', 'tointer': 'select the rows whose constructor record fuzzily matches to brm . select the row whose time / retired record of these rows is 3rd minimum . the driver record of this row is lucien bianchi .'}

eq { hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver } ; lucien bianchi } = true

select the rows whose constructor record fuzzily matches to brm . select the row whose time / retired record of these rows is 3rd minimum . the driver record of this row is lucien bianchi .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'constructor_6': 6, 'brm_7': 7, 'time / retired_8': 8, '3_9': 9, 'driver_10': 10, 'lucien bianchi_11': 11}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'constructor_6': 'constructor', 'brm_7': 'brm', 'time / retired_8': 'time / retired', '3_9': '3', 'driver_10': 'driver', 'lucien bianchi_11': 'lucien bianchi'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'constructor_6': [0], 'brm_7': [0], 'time / retired_8': [1], '3_9': [1], 'driver_10': [2], 'lucien bianchi_11': [3]}

['driver', 'constructor', 'laps', 'time / retired', 'grid']

[['jim clark', 'lotus - climax', '32', '2:23:34.8', '2'], ['jackie stewart', 'brm', '32', '+ 44.8 secs', '3'], ['bruce mclaren', 'cooper - climax', '31', '+ 1 lap', '9'], ['jack brabham', 'brabham - climax', '31', '+ 1 lap', '10'], ['graham hill', 'brm', '31', '+ 1 lap', '1'], ['richie ginther', 'honda', '31', '+ 1 lap', '4'], ['mike spence', 'lotus - climax', '31', '+ 1 lap', '12'], ['jo siffert', 'brabham - brm', '31', '+ 1 lap', '8'], ['lorenzo bandini', 'ferrari', '30', '+ 2 laps', '15'], ['dan gurney', 'brabham - climax', '30', '+ 2 laps', '5'], ['jochen rindt', 'cooper - climax', '29', '+ 3 laps', '14'], ['lucien bianchi', 'brm', '29', '+ 3 laps', '17'], ['innes ireland', 'lotus - brm', '27', '+ 5 laps', '16'], ['richard attwood', 'lotus - brm', '26', 'accident', '13'], ['masten gregory', 'brm', '12', 'fuel pump', '20'], ['jo bonnier', 'brabham - climax', '9', 'ignition', '7'], ['ronnie bucknum', 'honda', '9', 'gearbox', '11'], ['john surtees', 'ferrari', '5', 'engine', '6'], ['frank gardner', 'brabham - brm', '3', 'ignition', '19']]

the new adventures of huckleberry finn

https://en.wikipedia.org/wiki/The_New_Adventures_of_Huckleberry_Finn

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13403120-1.html.csv

unique

the only episode of the new adventures of huckleberry finn directed hollingsworth morse and written by george eckstein is huck of la mancha .

{'scope': 'subset', 'row': '2', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'george eckstein', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'hollingsworth morse'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'hollingsworth morse'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; director ; hollingsworth morse }', 'tointer': 'select the rows whose director record fuzzily matches to hollingsworth morse .'}, 'writer ( s )', 'george eckstein'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose director record fuzzily matches to hollingsworth morse . among these rows , select the rows whose writer ( s ) record fuzzily matches to george eckstein .', 'tostr': 'filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } }', 'tointer': 'select the rows whose director record fuzzily matches to hollingsworth morse . among these rows , select the rows whose writer ( s ) record fuzzily matches to george eckstein . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'hollingsworth morse'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; director ; hollingsworth morse }', 'tointer': 'select the rows whose director record fuzzily matches to hollingsworth morse .'}, 'writer ( s )', 'george eckstein'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose director record fuzzily matches to hollingsworth morse . among these rows , select the rows whose writer ( s ) record fuzzily matches to george eckstein .', 'tostr': 'filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein }'}, 'title'], 'result': 'huck of la mancha', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } ; title }'}, 'huck of la mancha'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } ; title } ; huck of la mancha }', 'tointer': 'the title record of this unqiue row is huck of la mancha .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } } ; eq { hop { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } ; title } ; huck of la mancha } } = true', 'tointer': 'select the rows whose director record fuzzily matches to hollingsworth morse . among these rows , select the rows whose writer ( s ) record fuzzily matches to george eckstein . there is only one such row in the table . the title record of this unqiue row is huck of la mancha .'}

and { only { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } } ; eq { hop { filter_eq { filter_eq { all_rows ; director ; hollingsworth morse } ; writer ( s ) ; george eckstein } ; title } ; huck of la mancha } } = true

select the rows whose director record fuzzily matches to hollingsworth morse . among these rows , select the rows whose writer ( s ) record fuzzily matches to george eckstein . there is only one such row in the table . the title record of this unqiue row is huck of la mancha .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'director_8': 8, 'hollingsworth morse_9': 9, 'writer (s)_10': 10, 'george eckstein_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'title_12': 12, 'huck of la mancha_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'director_8': 'director', 'hollingsworth morse_9': 'hollingsworth morse', 'writer (s)_10': 'writer ( s )', 'george eckstein_11': 'george eckstein', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'title_12': 'title', 'huck of la mancha_13': 'huck of la mancha'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'director_8': [0], 'hollingsworth morse_9': [0], 'writer (s)_10': [1], 'george eckstein_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'title_12': [3], 'huck of la mancha_13': [4]}

['', 'title', 'director', 'writer ( s )', 'originalairdate', 'repeatairdate ( s )', 'prodcode']

[['1', 'the magic shillelah', 'hollingsworth morse', 'frank crow leo rifkin', '15 / 09 / 1968', '16 / 03 / 1969 , 03 / 08 / 1969', '30 - 14'], ['2', 'huck of la mancha', 'hollingsworth morse', 'george eckstein', '22 / 09 / 1968', '26 / 01 / 1969', '30 - 9'], ['3', 'the terrible tempered kahleef', 'bruce bilson', 'joanna lee', '29 / 09 / 1968', '23 / 03 / 1969 , 24 / 08 / 1969', '30 - 7'], ['4', 'the little people', 'walter s burr', '( unknown )', '06 / 10 / 1968', '30 / 03 / 1969 , 31 / 08 / 1969', '30 - 3'], ['5', 'pirate island', 'byron haskin', 'kenneth l kolb', '13 / 10 / 1968', '13 / 04 / 1969 , 17 / 08 / 1969', '30 - 4'], ['6', 'the last labor of hercules', 'hollingsworth morse', 'david duncan', '20 / 10 / 1968', '06 / 04 / 1969 , 08 / 10 / 1969', '30 - 6'], ['7', "the gorgon 's head", 'hollingsworth morse', 'herman miller', '27 / 10 / 1968', 'n / a', '30 - 16'], ['8', 'the castle of evil', 'walter s burr', 'kenneth l kolb', '03 / 11 / 1968', '20 / 04 / 1969', '30 - 5'], ['9', 'hunting the hunter', 'hollingsworth morse', 'kenneth l kolb', '24 / 11 / 1968', '27 / 04 / 1969', '30 - 11'], ['10', 'the curse of thut', 'bruce bilson', 'david duncan', '01 / 12 / 1968', '22 / 06 / 1969', '30 - 2'], ['11', 'the ancient valley', 'hollingsworth morse', 'david duncan', '15 / 12 / 1968', '29 / 06 / 1969', '30 - 13'], ['12', 'menace in the ice', 'walter s burr', 'peter allan fields', '22 / 12 / 1968', '15 / 06 / 1969', '30 - 10'], ['13', 'the eye of doorgah', 'robert gist', 'herman miller', '29 / 12 / 1968', '13 / 07 / 1969', '30 - 1'], ['14', 'mission of captain mordecai', 'ezra stone', 'joanna lee', '05 / 01 / 1969', '11 / 03 / 1969', '30 - 8'], ['15', 'the jungle adventure', 'hollingsworth morse', 'bill lutz', '19 / 01 / 1969', '18 / 03 / 1969', '30 - 15'], ['16', 'son of the sun', 'hollingsworth morse', 'al c ward kenneth l kolb', '26 / 01 / 1969', '06 / 07 / 1969', '30 - 19'], ['17', 'prophecy of peril', 'hollingsworth morse', 'david duncan', '02 / 02 / 1969', '27 / 07 / 1969', '30 - 17'], ['18', 'strange experiment', 'hollingsworth morse', 'kenneth l kolb', '09 / 02 / 1969', '06 / 07 / 1969', '30 - 12'], ['19', 'the conquistador curse', 'bruce bilson', 'tom and helen august', '16 / 02 / 1969', '01 / 06 / 1969', '30 - 18']]

british rail classes 253 , 254 and 255

https://en.wikipedia.org/wiki/British_Rail_Classes_253%2C_254_and_255

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1131463-1.html.csv

superlative

the highest number of models made for a class among the british rail classes 253 , 254 and 255 was held by the class with unit numbers 254001 - 254032 .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '6', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'number'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; number }'}, 'unit numbers'], 'result': '254001 - 254032', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; number } ; unit numbers }'}, '254001 - 254032'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; number } ; unit numbers } ; 254001 - 254032 } = true', 'tointer': 'select the row whose number record of all rows is maximum . the unit numbers record of this row is 254001 - 254032 .'}

eq { hop { argmax { all_rows ; number } ; unit numbers } ; 254001 - 254032 } = true

select the row whose number record of all rows is maximum . the unit numbers record of this row is 254001 - 254032 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'number_5': 5, 'unit numbers_6': 6, '254001 - 254032_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'number_5': 'number', 'unit numbers_6': 'unit numbers', '254001 - 254032_7': '254001 - 254032'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'number_5': [0], 'unit numbers_6': [1], '254001 - 254032_7': [2]}

['class', 'operator', 'number', 'year built', 'cars per set', 'unit numbers']

[['class 253', 'br western region', '27', '1975 - 1977', '9', '253001 - 253027'], ['class 253', 'br western region', '13', '1978 - 1979', '9', '253028 - 253040'], ['class 253', 'br cross country', '18', '1981 - 1982', '9', '253041 - 253058'], ['class 254', 'br eastern region br scottish region', '32', '1977 - 1979', '10', '254001 - 254032'], ['class 254', 'br eastern region br scottish region', '4', '1982', '10', '254033 - 254037']]

wru division five south west

https://en.wikipedia.org/wiki/WRU_Division_Five_South_West

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17675675-2.html.csv

superlative

betws rfc had the highest number of points for among all clubs in the wru division five south west .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points for'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points for }'}, 'club'], 'result': 'betws rfc', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points for } ; club }'}, 'betws rfc'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points for } ; club } ; betws rfc } = true', 'tointer': 'select the row whose points for record of all rows is maximum . the club record of this row is betws rfc .'}

eq { hop { argmax { all_rows ; points for } ; club } ; betws rfc } = true

select the row whose points for record of all rows is maximum . the club record of this row is betws rfc .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points for_5': 5, 'club_6': 6, 'betws rfc_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points for_5': 'points for', 'club_6': 'club', 'betws rfc_7': 'betws rfc'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points for_5': [0], 'club_6': [1], 'betws rfc_7': [2]}

['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus']

[['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus'], ['betws rfc', '20', '0', '2', '727', '243', '111', '29', '14'], ['ystradgynlais rfc', '20', '0', '2', '667', '200', '107', '24', '15'], ['alltwen rfc', '20', '1', '4', '434', '237', '55', '21', '7'], ['new dock stars rfc', '20', '1', '8', '367', '318', '51', '38', '5'], ['pontardawe rfc', '20', '0', '10', '441', '381', '64', '51', '9'], ['trebanos rfc', '20', '1', '9', '441', '404', '51', '58', '5'], ['glais rfc', '20', '0', '11', '293', '325', '36', '42', '4'], ['gowerton rfc', '20', '0', '13', '313', '468', '38', '69', '2'], ['cwmtwrch rfc', '20', '2', '13', '261', '406', '28', '58', '0'], ['swansea uplands rfc', '20', '1', '17', '197', '574', '28', '89', '1'], ['bynea rfc', '20', '0', '18', '139', '724', '21', '111', '1']]

james vandenberg

https://en.wikipedia.org/wiki/James_Vandenberg

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16326065-2.html.csv

majority

james vandenberg spent the majority of years playing for the iowa team .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'iowa', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'team', 'iowa'], 'result': True, 'ind': 0, 'tointer': 'for the team records of all rows , most of them fuzzily match to iowa .', 'tostr': 'most_eq { all_rows ; team ; iowa } = true'}

most_eq { all_rows ; team ; iowa } = true

for the team records of all rows , most of them fuzzily match to iowa .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'team_3': 3, 'iowa_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'team_3': 'team', 'iowa_4': 'iowa'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'team_3': [0], 'iowa_4': [0]}

['year', 'team', 'attempts', 'completions', 'completion %', 'yards']

[['2008', 'iowa', 'redshirt', 'redshirt', 'redshirt', 'redshirt'], ['2009', 'iowa', '87', '42', '48.3 %', '470'], ['2010', 'iowa', '8', '5', '62.5 %', '45'], ['2011', 'iowa', '404', '237', '58.7 %', '3022'], ['2012', 'iowa', '389', '223', '57.3 %', '2249'], ['college totals', 'college totals', '888', '507', '57.1 %', '5786']]

fiba eurobasket 2007 squads

https://en.wikipedia.org/wiki/FIBA_EuroBasket_2007_squads

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12962773-9.html.csv

superlative

mithat demirel is the shortest person that played on the 2007 fiba eurobasket squads .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'height'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; height }'}, 'player'], 'result': 'mithat demirel', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; height } ; player }'}, 'mithat demirel'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; height } ; player } ; mithat demirel } = true', 'tointer': 'select the row whose height record of all rows is minimum . the player record of this row is mithat demirel .'}

eq { hop { argmin { all_rows ; height } ; player } ; mithat demirel } = true

select the row whose height record of all rows is minimum . the player record of this row is mithat demirel .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'height_5': 5, 'player_6': 6, 'mithat demirel_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'height_5': 'height', 'player_6': 'player', 'mithat demirel_7': 'mithat demirel'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'height_5': [0], 'player_6': [1], 'mithat demirel_7': [2]}

['player', 'height', 'position', 'year born', 'current club']

[['mithat demirel', '1.80', 'guard', '1978', 'unattached'], ['ademola okulaja', '2.02', 'forward', '1975', 'brose baskets'], ['stephen arigbabu', '2.05', 'center', '1972', 'olympia larissa'], ['robert garrett', '1.92', 'guard', '1977', 'brose baskets'], ['johannes herber', '1.97', 'guard', '1983', 'alba berlin'], ['steffen hamann', '1.93', 'guard', '1981', 'brose baskets'], ['demond greene', '1.85', 'guard', '1979', 'brose baskets'], ['pascal roller', '1.85', 'guard', '1976', 'deutsche bank skyliners'], ['guido grã ¼ nheid', '2.06', 'forward', '1982', 'hanzevast capitals'], ['patrick femerling', '2.13', 'center', '1975', 'alba berlin'], ['dirk nowitzki', '2.13', 'forward', '1978', 'dallas mavericks'], ['jan - hendrik jagla', '2.13', 'center', '1981', 'dkv joventut']]

$ 40 a day

https://en.wikipedia.org/wiki/%2440_a_Day

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1137274-3.html.csv

majority

all of the titles were directed by don colliver .

{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'don colliver', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'directed by', 'don colliver'], 'result': True, 'ind': 0, 'tointer': 'for the directed by records of all rows , all of them fuzzily match to don colliver .', 'tostr': 'all_eq { all_rows ; directed by ; don colliver } = true'}

all_eq { all_rows ; directed by ; don colliver } = true

for the directed by records of all rows , all of them fuzzily match to don colliver .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'directed by_3': 3, 'don colliver_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'directed by_3': 'directed by', 'don colliver_4': 'don colliver'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'directed by_3': [0], 'don colliver_4': [0]}

['title', 'directed by', 'written by', 'original air date', 'production code']

[['park city', 'don colliver', 'peter field rachael ray', 'august 20 , 2004', 'ad1c02'], ['grand canyon', 'don colliver', 'peter field rachael ray', 'august 27 , 2004', 'ad1c04'], ['durham', 'don colliver', 'peter field rachael ray', 'august 29 , 2004', 'ad1c08'], ['las vegas', 'don colliver', 'peter field rachael ray', 'september 10 , 2004', 'ad1c05'], ['bermuda', 'don colliver', 'peter field rachael ray', 'september 24 , 2004', 'ad1c06'], ['sun valley', 'don colliver', 'peter field rachael ray', 'october 15 , 2004', 'ad1c01'], ['chattanooga', 'don colliver', 'peter field rachael ray', 'october 29 , 2004', 'ad1c10'], ['hilton head', 'don colliver', 'peter field rachael ray', 'november 12 , 2004', 'ad1c07'], ['asheville', 'don colliver', 'peter field rachael ray', 'november 19 , 2004', 'ad1c09'], ['telluride', 'don colliver', 'peter field rachael ray', 'november 26 , 2004', 'ad1c03'], ['newport', 'don colliver', 'peter field rachael ray', 'december 17 , 2004', 'ad1c12'], ["martha 's vineyard", 'don colliver', 'peter field rachael ray', 'january 7 , 2005', 'ad1c13'], ['the hamptons', 'don colliver', 'peter field rachael ray', 'april 22 , 2005', 'ad1c11']]

2007 - 08 portland trail blazers season

https://en.wikipedia.org/wiki/2007%E2%80%9308_Portland_Trail_Blazers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11964047-10.html.csv

aggregation

during this period of the 2007-08 portland trail blazers season , the leading scorer scored 28.5 points on average .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '28.5', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'leading scorer'], 'result': '28.5', 'ind': 0, 'tostr': 'avg { all_rows ; leading scorer }'}, '28.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; leading scorer } ; 28.5 } = true', 'tointer': 'the average of the leading scorer record of all rows is 28.5 .'}

round_eq { avg { all_rows ; leading scorer } ; 28.5 } = true

the average of the leading scorer record of all rows is 28.5 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'leading scorer_4': 4, '28.5_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'leading scorer_4': 'leading scorer', '28.5_5': '28.5'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'leading scorer_4': [0], '28.5_5': [1]}

['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record', 'streak']

[['april 2', 'portland trail blazers', 'l 91 - 104', 'los angeles lakers', 'bryant : 36', 'staples center 18997', '38 - 37', 'l3'], ['april 3', 'houston rockets', 'l 95 - 86', 'portland trail blazers', 'mcgrady : 35', 'rose garden 19980', '38 - 38', 'l4'], ['april 6', 'san antonio spurs', 'l 72 - 65', 'portland trail blazers', 'duncan : 27', 'rose garden 19980', '38 - 39', 'l5'], ['april 8', 'los angeles lakers', 'w 103 - 112', 'portland trail blazers', 'bryant : 34', 'rose garden 20435', '39 - 39', 'w1'], ['april 11', 'portland trail blazers', 'l 86 - 103', 'sacramento kings', 'aldridge : 24', 'arco arena 13327', '39 - 40', 'l1'], ['april 12', 'dallas mavericks', 'w 105 - 108', 'portland trail blazers', 'nowitzki : 28', 'rose garden 19980', '40 - 40', 'w1'], ['april 15', 'memphis grizzlies', 'w 91 - 113', 'portland trail blazers', 'jones : 20', 'rose garden 19980', '41 - 40', 'w2'], ['april 16', 'portland trail blazers', 'l 91 - 100', 'phoenix suns', 'outlaw : 24', 'us airways center 18422', '41 - 41', 'l1']]

abdel sattar sabry

https://en.wikipedia.org/wiki/Abdel_Sattar_Sabry

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15553431-1.html.csv

superlative

the most recent competition at the abdel sattar sabry was on 13 july 2001 at alexandria stadium , alexandria .

{'scope': 'all', 'col_superlative': '1', 'row_superlative': '11', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'date'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; date }'}, 'venue'], 'result': 'alexandria stadium , alexandria', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; date } ; venue }'}, 'alexandria stadium , alexandria'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; date } ; venue } ; alexandria stadium , alexandria } = true', 'tointer': 'select the row whose date record of all rows is maximum . the venue record of this row is alexandria stadium , alexandria .'}

eq { hop { argmax { all_rows ; date } ; venue } ; alexandria stadium , alexandria } = true

select the row whose date record of all rows is maximum . the venue record of this row is alexandria stadium , alexandria .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'date_5': 5, 'venue_6': 6, 'alexandria stadium , alexandria_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'date_5': 'date', 'venue_6': 'venue', 'alexandria stadium , alexandria_7': 'alexandria stadium , alexandria'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'date_5': [0], 'venue_6': [1], 'alexandria stadium , alexandria_7': [2]}

['date', 'venue', 'score', 'result', 'competition']

[['30 june 1995', 'alexandria stadium , alexandria', '2 - 0', '6 - 0', '1996 africa cup of nations qualifier'], ['5 june 1997', 'cairo international stadium , cairo', '2 - 0', '2 - 0', 'friendly'], ['16 june 1997', 'seoul , south korea', '2 - 0', '2 - 0', '1997 korea cup'], ['17 august 1997', 'cairo international stadium , cairo', '3 - 0', '5 - 0', '1998 fifa world cup qualifier'], ['18 december 1997', 'aswan stadium , aswan', '7 - 2', '7 - 2', 'friendly'], ['25 july 1999', 'estadio azteca , mexico city , mexico', '1 - 0', '2 - 2', '1999 fifa confederations cup'], ['9 january 2001', 'cairo international stadium , cairo', '1 - 0', '3 - 0', 'friendly'], ['9 january 2001', 'cairo international stadium , cairo', '3 - 0', '3 - 0', 'friendly'], ['17 january 2001', 'cairo international stadium , cairo', '4 - 0', '4 - 0', '2002 african cup of nations qualifier'], ['11 march 2001', 'cairo international stadium , cairo', '2 - 0', '5 - 2', '2002 fifa world cup qualifier'], ['13 july 2001', 'alexandria stadium , alexandria', '8 - 2', '8 - 2', '2002 fifa world cup qualifier']]

united states house of representatives elections , 1996

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1996

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341472-45.html.csv

superlative

of the republicans serving in the 1996 united states house off representatives , the earliest was first elected in 1980 .

{'scope': 'subset', 'col_superlative': '1', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '3', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'republican'}}

{'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'district'], 'result': 'texas 8', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; party ; republican } ; district }', 'tointer': 'select the rows whose party record fuzzily matches to republican . the minimum district record of these rows is texas 8 .'}, 'texas 8'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; party ; republican } ; district } ; texas 8 } = true', 'tointer': 'select the rows whose party record fuzzily matches to republican . the minimum district record of these rows is texas 8 .'}

eq { min { filter_eq { all_rows ; party ; republican } ; district } ; texas 8 } = true

select the rows whose party record fuzzily matches to republican . the minimum district record of these rows is texas 8 .

3

3

{'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'party_5': 5, 'republican_6': 6, 'district_7': 7, 'texas 8_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'party_5': 'party', 'republican_6': 'republican', 'district_7': 'district', 'texas 8_8': 'texas 8'}

{'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'party_5': [0], 'republican_6': [0], 'district_7': [1], 'texas 8_8': [2]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['texas 5', 'john bryant', 'democratic', '1982', 'retired to run for us senate republican gain', 'pete sessions ( r ) 53.07 % john pouland ( d ) 46.93 %'], ['texas 8', 'jack fields', 'republican', '1980', 'retired republican hold', 'kevin brady ( r ) 59.11 % gene fontenot ( d ) 40.89 %'], ['texas 9', 'steve stockman', 'republican', '1994', 'lost re - election democratic gain', 'nick lampson ( d ) 52.83 % steve stockman ( r ) 47.16 %'], ['texas 19', 'larry combest', 'republican', '1984', 're - elected', 'larry combest ( r ) 80.37 % john sawyer ( d ) 19.63 %'], ['texas 22', 'tom delay', 'republican', '1984', 're - elected', 'tom delay ( r ) 68.11 % scott cunningham ( d ) 31.89 %']]

2008 - 09 cleveland cavaliers season

https://en.wikipedia.org/wiki/2008%E2%80%9309_Cleveland_Cavaliers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17325580-8.html.csv

majority

all games of the cleveland cavaliers ' in the 2008 - 09 season were played in the month of february .

{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'february', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'date', 'february'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to february .', 'tostr': 'all_eq { all_rows ; date ; february } = true'}

all_eq { all_rows ; date ; february } = true

for the date records of all rows , all of them fuzzily match to february .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'february_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'february_4': 'february'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'february_4': [0]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['46', 'february 1', 'detroit', 'w 90 - 80 ( ot )', 'lebron james ( 33 )', 'žydrūnas ilgauskas ( 8 )', 'lebron james ( 8 )', 'the palace of auburn hills 22076', '37 - 9'], ['47', 'february 3', 'toronto', 'w 101 - 83 ( ot )', 'lebron james ( 33 )', 'žydrūnas ilgauskas ( 8 )', 'maurice williams ( 9 )', 'quicken loans arena 20562', '38 - 9'], ['48', 'february 4', 'new york', 'w 107 - 102 ( ot )', 'lebron james ( 52 )', 'wally szczerbiak ( 13 )', 'lebron james ( 11 )', 'madison square garden 19763', '39 - 9'], ['49', 'february 8', 'la lakers', 'l 91 - 101 ( ot )', 'žydrūnas ilgauskas ( 22 )', 'žydrūnas ilgauskas , anderson varejão ( 9 )', 'lebron james ( 12 )', 'quicken loans arena 20562', '39 - 10'], ['50', 'february 10', 'indiana', 'l 95 - 96 ( ot )', 'lebron james ( 47 )', 'žydrūnas ilgauskas ( 11 )', 'lebron james ( 4 )', 'conseco fieldhouse 18165', '39 - 11'], ['51', 'february 11', 'phoenix', 'w 109 - 92 ( ot )', 'maurice williams ( 44 )', 'ben wallace ( 11 )', 'maurice williams ( 7 )', 'quicken loans arena 20562', '40 - 11'], ['52', 'february 18', 'toronto', 'w 93 - 76 ( ot )', 'žydrūnas ilgauskas ( 22 )', 'anderson varejão ( 14 )', 'lebron james ( 9 )', 'air canada centre 19800', '41 - 11'], ['53', 'february 20', 'milwaukee', 'w 111 - 103 ( ot )', 'lebron james ( 55 )', 'anderson varejão ( 7 )', 'lebron james ( 9 )', 'bradley center 18076', '42 - 11'], ['54', 'february 22', 'detroit', 'w 99 - 78 ( ot )', 'delonte west ( 25 )', 'žydrūnas ilgauskas ( 8 )', 'lebron james ( 9 )', 'quicken loans arena 20562', '43 - 11'], ['55', 'february 24', 'memphis', 'w 94 - 79 ( ot )', 'daniel gibson ( 19 )', 'j j hickson ( 9 )', 'lebron james ( 8 )', 'quicken loans arena 20562', '44 - 11'], ['56', 'february 26', 'houston', 'l 74 - 93 ( ot )', 'lebron james , maurice williams ( 21 )', 'žydrūnas ilgauskas ( 13 )', 'maurice williams ( 4 )', 'toyota center 18399', '44 - 12'], ['57', 'february 27', 'san antonio', 'w 97 - 86 ( ot )', 'lebron james ( 30 )', 'lebron james ( 14 )', 'delonte west ( 5 )', 'at & t center 18797', '45 - 12']]

vladimir koman

https://en.wikipedia.org/wiki/Vladimir_Koman

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10521952-3.html.csv

aggregation

there were eight points scored in the matches that were played in 2010 .

{'scope': 'subset', 'col': '3', 'type': 'sum', 'result': '8', 'subset': {'col': '1', 'criterion': 'fuzzily_match', 'value': '2010'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '2010'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 2010 }', 'tointer': 'select the rows whose date record fuzzily matches to 2010 .'}, 'score'], 'result': '8', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; date ; 2010 } ; score }'}, '8'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; date ; 2010 } ; score } ; 8 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 2010 . the sum of the score record of these rows is 8 .'}

round_eq { sum { filter_eq { all_rows ; date ; 2010 } ; score } ; 8 } = true

select the rows whose date record fuzzily matches to 2010 . the sum of the score record of these rows is 8 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, '2010_6': 6, 'score_7': 7, '8_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', '2010_6': '2010', 'score_7': 'score', '8_8': '8'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '2010_6': [0], 'score_7': [1], '8_8': [2]}

['date', 'venue', 'score', 'result', 'competition']

[['7 september 2010', 'szusza stadium , budapest', '2 - 0', '2 - 1', 'uefa euro 2012 qualifying'], ['8 october 2010', 'puskás stadium , budapest', '6 - 0', '8 - 0', 'uefa euro 2012 qualifying'], ['7 june 2011', 'stadio olimpico , serravalle', '3 - 0', '3 - 0', 'uefa euro 2012 qualifying'], ['10 august 2011', 'puskás stadium , budapest', '1 - 0', '4 - 0', 'international friendly'], ['11 september 2011', 'puskás stadium , budapest', '4 - 0', '5 - 0', 'international friendly'], ['7 september 2012', 'estadi comunal , andorra la vella', '5 - 0', '5 - 0', '2014 fifa world cup qualifying'], ['16 october 2012', 'puskás stadium , budapest', '1 - 1', '3 - 1', '2014 fifa world cup qualifying']]

list of csi : ny characters

https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11240028-1.html.csv

superlative

on csi : ny , the character aiden burn , csi detective , appeared on the least number of episodes .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '9', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'episodes'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; episodes }'}, 'character'], 'result': 'aiden burn csi detective', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; episodes } ; character }'}, 'aiden burn csi detective'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; episodes } ; character } ; aiden burn csi detective } = true', 'tointer': 'select the row whose episodes record of all rows is minimum . the character record of this row is aiden burn csi detective .'}

eq { hop { argmin { all_rows ; episodes } ; character } ; aiden burn csi detective } = true

select the row whose episodes record of all rows is minimum . the character record of this row is aiden burn csi detective .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'episodes_5': 5, 'character_6': 6, 'aiden burn csi detective_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'episodes_5': 'episodes', 'character_6': 'character', 'aiden burn csi detective_7': 'aiden burn csi detective'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'episodes_5': [0], 'character_6': [1], 'aiden burn csi detective_7': [2]}

['character', 'portrayed by', 'first appearance', 'last appearance', 'duration', 'episodes']

[['mac taylor csi detective', 'gary sinise', 'blink 1 , 2 , 3', 'today is life', '1.01 - 9.17', '197'], ['jo danville csi detective', 'sela ward', 'the 34th floor', 'today is life', '7.01 - 9.17', '57'], ['danny messer csi detective', 'carmine giovinazzo', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['lindsay monroe messer csi detective', 'anna belknap', 'zoo york', 'today is life', '2.03 - 9.17', '172 4'], ['dr sid hammerback chief medical examiner', 'robert joy', 'dancing with the fishes', 'today is life', '2.05 - 9.17', '168 4'], ['adam ross lab technician', 'a j buckley', 'bad beat', 'today is life', '2.08 - 9.17', '141 4'], ['dr sheldon hawkes csi', 'hill harper', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['don flack homicide detective', 'eddie cahill', 'blink', 'today is life', '1.01 - 9.17', '197'], ['aiden burn csi detective', 'vanessa ferlito', 'blink 1', 'heroes', '1.01 - 2.02 , 2.23', '26'], ['stella bonasera csi detective', 'melina kanakaredes', 'blink 1', 'vacation getaway', '1.01 - 6.22', '140']]

2009 - 10 louisville cardinals men 's basketball team

https://en.wikipedia.org/wiki/2009%E2%80%9310_Louisville_Cardinals_men%27s_basketball_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25118909-3.html.csv

unique

jared swopshire is the only player on the 2009 - 10 louisville cardinals men 's basketball team that formerly attended img academy .

{'scope': 'all', 'row': '13', 'col': '7', 'col_other': '1', 'criterion': 'equal', 'value': 'img academy', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'former school', 'img academy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose former school record fuzzily matches to img academy .', 'tostr': 'filter_eq { all_rows ; former school ; img academy }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; former school ; img academy } }', 'tointer': 'select the rows whose former school record fuzzily matches to img academy . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'former school', 'img academy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose former school record fuzzily matches to img academy .', 'tostr': 'filter_eq { all_rows ; former school ; img academy }'}, 'name'], 'result': 'jared swopshire', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; former school ; img academy } ; name }'}, 'jared swopshire'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; former school ; img academy } ; name } ; jared swopshire }', 'tointer': 'the name record of this unqiue row is jared swopshire .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; former school ; img academy } } ; eq { hop { filter_eq { all_rows ; former school ; img academy } ; name } ; jared swopshire } } = true', 'tointer': 'select the rows whose former school record fuzzily matches to img academy . there is only one such row in the table . the name record of this unqiue row is jared swopshire .'}

and { only { filter_eq { all_rows ; former school ; img academy } } ; eq { hop { filter_eq { all_rows ; former school ; img academy } ; name } ; jared swopshire } } = true

select the rows whose former school record fuzzily matches to img academy . there is only one such row in the table . the name record of this unqiue row is jared swopshire .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'former school_7': 7, 'img academy_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'jared swopshire_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'former school_7': 'former school', 'img academy_8': 'img academy', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'jared swopshire_10': 'jared swopshire'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'former school_7': [0], 'img academy_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'jared swopshire_10': [3]}

['name', '-', 'position', 'height', 'weight', 'year', 'former school', 'hometown']

[['chris brickley', '11', 'guard', '6 - 4', '175', 'senior', 'northeastern university', 'manchester , nh'], ['rakeem buckles', '4', 'forward', '6 - 8', '200', 'freshman', 'pace', 'miami , fl'], ['reginald delk', '12', 'guard', '6 - 4', '175', 'senior', 'mississippi state university', 'jackson , tn'], ['george goode', '22', 'guard', '6 - 8', '205', 'sophomore', 'raytown south', 'raytown , mo'], ['terrence jennings', '23', 'forward', '6 - 10', '225', 'sophomore', 'notre dame prep', 'sacramento , ca'], ['preston knowles', '2', 'guard', '6 - 1', '170', 'junior', 'george rogers clark', 'winchester , ky'], ['kyle kuric', '14', 'guard', '6 - 4', '175', 'sophomore', 'reitz memorial', 'evansville , in'], ['mike marra', '33', 'guard', '6 - 4', '190', 'freshman', 'northfield mt hermon', 'esmond , ri'], ['samardo samuels', '15', 'forward', '6 - 8', '240', 'sophomore', 'st benedict', 'trelawny , jamaica'], ['peyton siva', '3', 'guard', '5 - 10', '165', 'freshman', 'franklin', 'seattle , wa'], ['jerry smith', '34', 'guard', '6 - 1', '200', 'senior', 'east', 'wauwatosa , wi'], ['edgar sosa', '10', 'guard', '6 - 1', '200', 'senior', 'rice', 'bronx , ny'], ['jared swopshire', '21', 'forward', '6 - 7', '215', 'sophomore', 'img academy', 'st louis , mo'], ['stephan van treese', '44', 'forward', '6 - 8', '220', 'freshman', 'lawrence north', 'indianapolis , in']]

united states house of representatives elections , 2000

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-21.html.csv

count

five of the incumbents from the districts of massachusetts were unopposed in the 2000 united states house of representatives elections .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'unopposed', 'result': '5', 'col': '6', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'candidates', 'unopposed'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose candidates record fuzzily matches to unopposed .', 'tostr': 'filter_eq { all_rows ; candidates ; unopposed }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; candidates ; unopposed } }', 'tointer': 'select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 5 } = true', 'tointer': 'select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 5 } = true

select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'candidates_5': 5, 'unopposed_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'candidates_5': 'candidates', 'unopposed_6': 'unopposed', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'candidates_5': [0], 'unopposed_6': [0], '5_7': [2]}

['district', 'incumbent', 'party', 'first elected', 'results', 'candidates']

[['massachusetts 1', 'john olver', 'democratic', '1991', 're - elected', 'john olver ( d ) 69 % peter abair ( r ) 30 %'], ['massachusetts 2', 'richard neal', 'democratic', '1988', 're - elected', 'richard neal ( d ) unopposed'], ['massachusetts 3', 'jim mcgovern', 'democratic', '1996', 're - elected', 'jim mcgovern ( d ) unopposed'], ['massachusetts 4', 'barney frank', 'democratic', '1980', 're - elected', 'barney frank ( d ) 71 % martin travis ( r ) 21 %'], ['massachusetts 5', 'marty meehan', 'democratic', '1992', 're - elected', 'marty meehan ( d ) unopposed'], ['massachusetts 6', 'john f tierney', 'democratic', '1996', 're - elected', 'john f tierney ( d ) 71 % paul mccarthy ( r ) 29 %'], ['massachusetts 7', 'ed markey', 'democratic', '1976', 're - elected', 'ed markey ( d ) unopposed'], ['massachusetts 8', 'mike capuano', 'democratic', '1998', 're - elected', 'mike capuano ( d ) unopposed'], ['massachusetts 9', 'joe moakley', 'democratic', '1972', 're - elected', 'joe moakley ( d ) 78 % janet jeghelian ( r ) 20 %']]

alberto heredia ceballos

https://en.wikipedia.org/wiki/Alberto_Heredia_Ceballos

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11012004-1.html.csv

majority

alberto heredia cebellos spent most of his years in the segunda division b league .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'segunda división b', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'league', 'segunda división b'], 'result': True, 'ind': 0, 'tointer': 'for the league records of all rows , most of them fuzzily match to segunda división b .', 'tostr': 'most_eq { all_rows ; league ; segunda división b } = true'}

most_eq { all_rows ; league ; segunda división b } = true

for the league records of all rows , most of them fuzzily match to segunda división b .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'league_3': 3, 'segunda división b_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'league_3': 'league', 'segunda división b_4': 'segunda división b'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'league_3': [0], 'segunda división b_4': [0]}

['season', 'team', 'country', 'league', 'level', 'apps', 'goals']

[['2006 - 07', 'sevilla b', 'spain', 'segunda división b', '3', '3', '0'], ['2007 - 08', 'marbella', 'spain', 'segunda división b', '3', '11', '0'], ['2007 - 08', 'burgos', 'spain', 'segunda división b', '3', '15', '1'], ['2008 - 09', 'santa eulàlia', 'spain', 'segunda división b', '3', '33', '4'], ['2009 - 10', 'terrassa', 'spain', 'segunda división b', '3', '18', '0'], ['2009 - 10', 'getafe b', 'spain', 'tercera división', '4', '6', '0'], ['2010 - 11', 'roteña', 'spain', 'primera andaluza', '5', '5', '0'], ['2011 - 12', 'chieti', 'italy', 'lega pro seconda divisione', '4', '4', '0'], ['2011 - 12', 'lorca atlético', 'spain', 'segunda división b', '3', '1', '0'], ['2012', 'fc kairat', 'kazakhstan', 'premier league', '1', '18', '0']]

wayne gardner

https://en.wikipedia.org/wiki/Wayne_Gardner

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1861430-3.html.csv

count

wayne gardner ended a total of four years without any wins .

{'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '4', 'col': '6', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; wins ; 0 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; wins ; 0 } }', 'tointer': 'select the rows whose wins record is equal to 0 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; wins ; 0 } } ; 4 } = true', 'tointer': 'select the rows whose wins record is equal to 0 . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; wins ; 0 } } ; 4 } = true

select the rows whose wins record is equal to 0 . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'wins_5': 5, '0_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'wins_5': 'wins', '0_6': '0', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'wins_5': [0], '0_6': [0], '4_7': [2]}

['year', 'class', 'team', 'machine', 'points', 'wins']

[['1983', '500cc', 'honda britain', 'ns500', '0', '0'], ['1984', '500cc', 'honda britain', 'ns500', '33', '0'], ['1985', '500cc', 'rothmans honda', 'nsr500', '73', '0'], ['1986', '500cc', 'rothmans honda', 'nsr500', '117', '3'], ['1987', '500cc', 'rothmans honda', 'nsr500', '178', '7'], ['1988', '500cc', 'rothmans honda', 'nsr500', '229', '4'], ['1989', '500cc', 'rothmans honda', 'nsr500', '67', '1'], ['1990', '500cc', 'rothmans honda', 'nsr500', '138', '2'], ['1991', '500cc', 'rothmans honda', 'nsr500', '161', '0'], ['1992', '500cc', 'rothmans honda', 'nsr500', '78', '1']]

1970 cfl draft

https://en.wikipedia.org/wiki/1970_CFL_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26996293-2.html.csv

comparative

player john senst was drafted before player paul brown in the 1970 cfl draft .

{'row_1': '1', 'row_2': '7', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'john senst'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to john senst .', 'tostr': 'filter_eq { all_rows ; player ; john senst }'}, 'pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; john senst } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to john senst . take the pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'paul brown'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to paul brown .', 'tostr': 'filter_eq { all_rows ; player ; paul brown }'}, 'pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; paul brown } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to paul brown . take the pick record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; john senst } ; pick } ; hop { filter_eq { all_rows ; player ; paul brown } ; pick } } = true', 'tointer': 'select the rows whose player record fuzzily matches to john senst . take the pick record of this row . select the rows whose player record fuzzily matches to paul brown . take the pick record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; player ; john senst } ; pick } ; hop { filter_eq { all_rows ; player ; paul brown } ; pick } } = true

select the rows whose player record fuzzily matches to john senst . take the pick record of this row . select the rows whose player record fuzzily matches to paul brown . take the pick record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'john senst_8': 8, 'pick_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'paul brown_12': 12, 'pick_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'john senst_8': 'john senst', 'pick_9': 'pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'paul brown_12': 'paul brown', 'pick_13': 'pick'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'john senst_8': [0], 'pick_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'paul brown_12': [1], 'pick_13': [3]}

['pick', 'cfl team', 'player', 'position', 'college']

[['10', 'winnipeg ( 2 )', 'john senst', 'fl', 'simon fraser'], ['11', 'montreal ( 1 )', 'burns mcpherson', 'hb', 'st francis xavier'], ['12', 'edmonton ( 2 )', 'jim henshall', 'hb', 'western'], ['13', 'bc lions ( 2 )', "tony d'aloisio", 'fb', 'windsor'], ['14', 'winnipeg ( 3 ) via hamilton', 'rick sugden', 'hb', 'simon fraser'], ['15', 'calgary ( 3 )', 'don lumb', 'ot', 'british columbia'], ['16', 'toronto ( 1 )', 'paul brown', 'ot', 'waterloo lutheran'], ['17', 'saskatchewan ( 2 )', 'andre rancourt', 'de', 'ottawa']]

stewart cink

https://en.wikipedia.org/wiki/Stewart_Cink

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1580725-6.html.csv

majority

in tournaments where stewart cink had zero wins , he had 1top-5 in all of them .

{'scope': 'subset', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '1', 'subset': {'col': '2', 'criterion': 'equal', 'value': '0'}}

{'func': 'all_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; wins ; 0 }', 'tointer': 'select the rows whose wins record is equal to 0 .'}, 'top - 5', '1'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose wins record is equal to 0 . for the top - 5 records of these rows , all of them are equal to 1 .', 'tostr': 'all_eq { filter_eq { all_rows ; wins ; 0 } ; top - 5 ; 1 } = true'}

all_eq { filter_eq { all_rows ; wins ; 0 } ; top - 5 ; 1 } = true

select the rows whose wins record is equal to 0 . for the top - 5 records of these rows , all of them are equal to 1 .

2

2

{'all_eq_1': 1, 'result_2': 2, 'filter_eq_0': 0, 'all_rows_3': 3, 'wins_4': 4, '0_5': 5, 'top - 5_6': 6, '1_7': 7}

{'all_eq_1': 'all_eq', 'result_2': 'true', 'filter_eq_0': 'filter_eq', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '0_5': '0', 'top - 5_6': 'top - 5', '1_7': '1'}

{'all_eq_1': [2], 'result_2': [], 'filter_eq_0': [1], 'all_rows_3': [0], 'wins_4': [0], '0_5': [0], 'top - 5_6': [1], '1_7': [1]}

['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made']

[['masters tournament', '0', '1', '2', '8', '16', '11'], ['us open', '0', '1', '3', '7', '18', '12'], ['the open championship', '1', '1', '2', '3', '15', '11'], ['pga championship', '0', '1', '2', '5', '16', '10'], ['totals', '1', '4', '9', '23', '65', '44']]

galaxy angel

https://en.wikipedia.org/wiki/Galaxy_Angel

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1714685-1.html.csv

ordinal

the first broadcast of galaxy angel in the us was on june 27 , 2006 .

{'row': '1', 'col': '7', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'us release date', '1'], 'result': '27 june 2006', 'ind': 0, 'tostr': 'nth_min { all_rows ; us release date ; 1 }', 'tointer': 'the 1st minimum us release date record of all rows is 27 june 2006 .'}, '27 june 2006'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 }', 'tointer': 'the 1st minimum us release date record of all rows is 27 june 2006 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'us release date', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; us release date ; 1 }'}, 'title'], 'result': 'galaxy angel', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; us release date ; 1 } ; title }'}, 'galaxy angel'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel }', 'tointer': 'the title record of the row with 1st minimum us release date record is galaxy angel .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 } ; eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel } } = true', 'tointer': 'the 1st minimum us release date record of all rows is 27 june 2006 . the title record of the row with 1st minimum us release date record is galaxy angel .'}

and { eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 } ; eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel } } = true

the 1st minimum us release date record of all rows is 27 june 2006 . the title record of the row with 1st minimum us release date record is galaxy angel .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'us release date_8': 8, '1_9': 9, '27 june 2006_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'us release date_12': 12, '1_13': 13, 'title_14': 14, 'galaxy angel_15': 15}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'us release date_8': 'us release date', '1_9': '1', '27 june 2006_10': '27 june 2006', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'us release date_12': 'us release date', '1_13': '1', 'title_14': 'title', 'galaxy angel_15': 'galaxy angel'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'us release date_8': [0], '1_9': [0], '27 june 2006_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'us release date_12': [2], '1_13': [2], 'title_14': [3], 'galaxy angel_15': [4]}

['series', 'title', 'broadcasts ( tv ) 1', 'episodes ( tv + extra ) 2', 'directors', 'aired in japan 3', 'us release date']

[['1', 'galaxy angel', '24', '24 + 2', 'morio asaka , yoshimitsu ohashi', '7 april 2001 to 29 september 2001', '27 june 2006'], ['2', 'galaxy angel z', '9', '18 + 1', 'morio asaka , yoshimitsu ohashi', '3 february 2002 to 31 march 2002', '25 july 2006'], ['3 4', 'galaxy angel a', '13', '26', 'shigehito takayanagi', '10 november 2002 to 29 december 2002', '27 december 2006'], ['3 4', 'galaxy angel aa', '13', '25 + 2', 'shigehito takayanagi', '5 january 2003 to 30 march 2003', '19 august 2008'], ['3 4', 'galaxy angel s 5', '1', '2', 'shigehito takayanagi', '21 december 2003', '6 may 2008'], ['4', 'galaxy angel x', '13', '26', 'shigehito takayanagi', '7 july 2004 to 29 september 2004', 'september 2 , 2008']]

united states house of representatives elections , 2006

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-26.html.csv

comparative

sam graves has been in office 4 years longer than russ carnahan .

{'row_1': '6', 'row_2': '3', 'col': '4', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '4', 'bigger': 'row2'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'sam graves'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to sam graves .', 'tostr': 'filter_eq { all_rows ; incumbent ; sam graves }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to sam graves . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'russ carnahan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to russ carnahan .', 'tostr': 'filter_eq { all_rows ; incumbent ; russ carnahan }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to russ carnahan . take the first elected record of this row .'}], 'result': '-4', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected } }'}, '-4'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected } } ; -4 } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to sam graves . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to russ carnahan . take the first elected record of this row . the second record is 4 larger than the first record .'}

eq { diff { hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected } } ; -4 } = true

select the rows whose incumbent record fuzzily matches to sam graves . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to russ carnahan . take the first elected record of this row . the second record is 4 larger than the first record .

6

6

{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'incumbent_8': 8, 'sam graves_9': 9, 'first elected_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'incumbent_12': 12, 'russ carnahan_13': 13, 'first elected_14': 14, '-4_15': 15}

{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'incumbent_8': 'incumbent', 'sam graves_9': 'sam graves', 'first elected_10': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'incumbent_12': 'incumbent', 'russ carnahan_13': 'russ carnahan', 'first elected_14': 'first elected', '-4_15': '-4'}

{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'incumbent_8': [0], 'sam graves_9': [0], 'first elected_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'incumbent_12': [1], 'russ carnahan_13': [1], 'first elected_14': [3], '-4_15': [5]}

['district', 'incumbent', 'party', 'first elected', 'results']

[['missouri 1', 'william lacy clay jr', 'democratic', '2000', 're - elected'], ['missouri 2', 'todd akin', 'republican', '2000', 're - elected'], ['missouri 3', 'russ carnahan', 'democratic', '2004', 're - elected'], ['missouri 4', 'ike skelton', 'democratic', '1976', 're - elected'], ['missouri 5', 'emanuel cleaver', 'democratic', '2004', 're - elected'], ['missouri 6', 'sam graves', 'republican', '2000', 're - elected'], ['missouri 7', 'roy blunt', 'republican', '1996', 're - elected'], ['missouri 8', 'jo ann emerson', 'republican', '1996', 're - elected'], ['missouri 9', 'kenny hulshof', 'republican', '1996', 're - elected']]

1930 vfl season

https://en.wikipedia.org/wiki/1930_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767641-1.html.csv

aggregation

in the vfl 's 1930 season on may 3 , average crowd attendance at each venue was 18,333.33 .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '18,333.33', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '18,333.33', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '18,333.33'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 18,333.33 } = true', 'tointer': 'the average of the crowd record of all rows is 18,333.33 .'}

round_eq { avg { all_rows ; crowd } ; 18,333.33 } = true

the average of the crowd record of all rows is 18,333.33 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '18,333.33_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '18,333.33_5': '18,333.33'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '18,333.33_5': [1]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['hawthorn', '16.7 ( 103 )', 'st kilda', '14.8 ( 92 )', 'glenferrie oval', '18000', '3 may 1930'], ['geelong', '18.13 ( 121 )', 'north melbourne', '2.7 ( 19 )', 'corio oval', '9500', '3 may 1930'], ['fitzroy', '12.14 ( 86 )', 'footscray', '11.6 ( 72 )', 'brunswick street oval', '18500', '3 may 1930'], ['south melbourne', '9.16 ( 70 )', 'melbourne', '13.17 ( 95 )', 'lake oval', '16000', '3 may 1930'], ['richmond', '7.18 ( 60 )', 'collingwood', '10.14 ( 74 )', 'punt road oval', '32000', '3 may 1930'], ['essendon', '7.9 ( 51 )', 'carlton', '8.10 ( 58 )', 'windy hill', '26000', '3 may 1930']]

1987 - 88 bradford city a.f.c. season

https://en.wikipedia.org/wiki/1987%E2%80%9388_Bradford_City_A.F.C._season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18998832-5.html.csv

unique

in the 1987 - 88 bradford city a.f.c. season , their only away game with attendance more than 10,000 was on 19 january 1988 .

{'scope': 'subset', 'row': '6', 'col': '6', 'col_other': '2', 'criterion': 'greater_than', 'value': '10000', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'away'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'away'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; venue ; away }', 'tointer': 'select the rows whose venue record fuzzily matches to away .'}, 'attendance', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to away . among these rows , select the rows whose attendance record is greater than 10000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } }', 'tointer': 'select the rows whose venue record fuzzily matches to away . among these rows , select the rows whose attendance record is greater than 10000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'away'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; venue ; away }', 'tointer': 'select the rows whose venue record fuzzily matches to away .'}, 'attendance', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to away . among these rows , select the rows whose attendance record is greater than 10000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 }'}, 'date'], 'result': '19 january 1988', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } ; date }'}, '19 january 1988'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } ; date } ; 19 january 1988 }', 'tointer': 'the date record of this unqiue row is 19 january 1988 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } ; date } ; 19 january 1988 } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to away . among these rows , select the rows whose attendance record is greater than 10000 . there is only one such row in the table . the date record of this unqiue row is 19 january 1988 .'}

and { only { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; venue ; away } ; attendance ; 10000 } ; date } ; 19 january 1988 } } = true

select the rows whose venue record fuzzily matches to away . among these rows , select the rows whose attendance record is greater than 10000 . there is only one such row in the table . the date record of this unqiue row is 19 january 1988 .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'venue_8': 8, 'away_9': 9, 'attendance_10': 10, '10000_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, '19 january 1988_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'venue_8': 'venue', 'away_9': 'away', 'attendance_10': 'attendance', '10000_11': '10000', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', '19 january 1988_13': '19 january 1988'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'venue_8': [0], 'away_9': [0], 'attendance_10': [1], '10000_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], '19 january 1988_13': [4]}

['round ( leg )', 'date', 'opponent', 'venue', 'result', 'attendance']

[['2 ( 1 )', '22 september 1987', 'fulham', 'away', '5 - 1', '4357'], ['2 ( 2 )', '7 october 1987', 'fulham', 'home', '2 - 1', '6408'], ['3', '27 october 1987', 'charlton athletic', 'away', '1 - 0', '3629'], ['4', '18 november 1987', 'reading', 'away', '0 - 0', '6784'], ['4r', '24 november 1987', 'reading', 'home', '1 - 0', '10448'], ['5', '19 january 1988', 'luton town', 'away', '0 - 2', '11022']]

rowing at the 2008 summer olympics - women 's single sculls

https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_single_sculls

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662695-8.html.csv

ordinal

gabriella bascelli had the third fastest time in the 2008 summer olympics - women 's single sculls .

{'row': '3', 'col': '4', 'order': '3', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 3 }'}, 'athlete'], 'result': 'gabriella bascelli', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 3 } ; athlete }'}, 'gabriella bascelli'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 3 } ; athlete } ; gabriella bascelli } = true', 'tointer': 'select the row whose time record of all rows is 3rd minimum . the athlete record of this row is gabriella bascelli .'}

eq { hop { nth_argmin { all_rows ; time ; 3 } ; athlete } ; gabriella bascelli } = true

select the row whose time record of all rows is 3rd minimum . the athlete record of this row is gabriella bascelli .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '3_6': 6, 'athlete_7': 7, 'gabriella bascelli_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '3_6': '3', 'athlete_7': 'athlete', 'gabriella bascelli_8': 'gabriella bascelli'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '3_6': [0], 'athlete_7': [1], 'gabriella bascelli_8': [2]}

['rank', 'athlete', 'country', 'time', 'notes']

[['1', 'michelle guerette', 'united states', '7:28.91', 'sa / b'], ['2', 'julia michalska', 'poland', '7:31.90', 'sa / b'], ['3', 'gabriella bascelli', 'italy', '7:36.68', 'sa / b'], ['4', 'nuria domã\xadnguez', 'spain', '7:49.60', 'sc / d'], ['5', 'inga dudchenko', 'kazakhstan', '8:15.88', 'sc / d'], ['6', 'elana hill', 'zimbabwe', '8:20.84', 'sc / d']]

united states house of representatives elections , 1952

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1952

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342149-38.html.csv

majority

most of the incumbents in the house of representatives in 1952 were re-elected .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 're - elected', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to re - elected .', 'tostr': 'most_eq { all_rows ; result ; re - elected } = true'}

most_eq { all_rows ; result ; re - elected } = true

for the result records of all rows , most of them fuzzily match to re - elected .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 're - elected_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 're - elected_4': 're - elected'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 're - elected_4': [0]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['pennsylvania 3', 'hardie scott', 'republican', '1946', 'retired democratic gain', 'james a byrne ( d ) 58.4 % morton witkin ( r ) 41.6 %'], ['pennsylvania 9', 'paul b dague', 'republican', '1946', 're - elected', 'paul b dague ( r ) 66.2 % philip e ragan ( d ) 33.8 %'], ['pennsylvania 12', 'ivor d fenton', 'republican', '1938', 're - elected', 'ivor d fenton ( r ) 60.7 % peter krehel ( d ) 39.3 %'], ['pennsylvania 23', 'leon h gavin redistricted from 19th', 'republican', '1942', 're - elected', 'leon h gavin ( r ) 67.8 % fred c barr ( d ) 32.2 %'], ['pennsylvania 25', 'louis e graham', 'republican', '1938', 're - elected', 'louis e graham ( r ) 50.4 % frank m clark ( d ) 49.6 %']]

television in italy

https://en.wikipedia.org/wiki/Television_in_Italy

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15887683-15.html.csv

unique

erde und mensch is the only service available that offers salute content .

{'scope': 'all', 'row': '10', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'salute', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'content', 'salute'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose content record fuzzily matches to salute .', 'tostr': 'filter_eq { all_rows ; content ; salute }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; content ; salute } }', 'tointer': 'select the rows whose content record fuzzily matches to salute . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'content', 'salute'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose content record fuzzily matches to salute .', 'tostr': 'filter_eq { all_rows ; content ; salute }'}, 'television service'], 'result': 'erde und mensch', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; content ; salute } ; television service }'}, 'erde und mensch'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch }', 'tointer': 'the television service record of this unqiue row is erde und mensch .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; content ; salute } } ; eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch } } = true', 'tointer': 'select the rows whose content record fuzzily matches to salute . there is only one such row in the table . the television service record of this unqiue row is erde und mensch .'}

and { only { filter_eq { all_rows ; content ; salute } } ; eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch } } = true

select the rows whose content record fuzzily matches to salute . there is only one such row in the table . the television service record of this unqiue row is erde und mensch .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'content_7': 7, 'salute_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'television service_9': 9, 'erde und mensch_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'content_7': 'content', 'salute_8': 'salute', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'television service_9': 'television service', 'erde und mensch_10': 'erde und mensch'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'content_7': [0], 'salute_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'television service_9': [2], 'erde und mensch_10': [3]}

['television service', 'country', 'language', 'content', 'hdtv', 'package / option']

[['telepace', 'italy', 'italian', 'religione', 'no', 'no ( fta )'], ['daystar television network', 'italy', 'english', 'religione', 'no', 'no ( fta )'], ['padre pio tv', 'italy', 'italian', 'religione', 'no', 'no ( fta )'], ['the word network', 'united kingdom', 'english', 'religione', 'no', 'no ( fta )'], ['inspiration', 'united kingdom', 'english', 'religione', 'no', 'no ( fta )'], ['ewtn', 'united kingdom', 'english', 'religione', 'no', 'no ( fta )'], ['tbne', 'italy', 'italian', 'religione', 'no', 'no ( fta )'], ['sender neu jerusalem', 'germany', 'german', 'religione', 'no', 'no ( fta )'], ['trsp', 'italy', 'italian', 'religione', 'no', 'no ( fta )'], ['erde und mensch', 'germany', 'german', 'salute', 'no', 'no ( fta )']]

list of teachers ( uk tv series ) episodes

https://en.wikipedia.org/wiki/List_of_Teachers_%28UK_TV_series%29_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18335117-5.html.csv

ordinal

for the uk tv series teachers , the 2nd to last episode to air had production code 407 .

{'row': '7', 'col': '6', 'order': '2', 'col_other': '7', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'original air date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; original air date ; 2 }'}, 'production code'], 'result': '407', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; original air date ; 2 } ; production code }'}, '407'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; production code } ; 407 } = true', 'tointer': 'select the row whose original air date record of all rows is 2nd maximum . the production code record of this row is 407 .'}

eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; production code } ; 407 } = true

select the row whose original air date record of all rows is 2nd maximum . the production code record of this row is 407 .

3

3

{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'original air date_5': 5, '2_6': 6, 'production code_7': 7, '407_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'original air date_5': 'original air date', '2_6': '2', 'production code_7': 'production code', '407_8': '407'}

{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'original air date_5': [0], '2_6': [0], 'production code_7': [1], '407_8': [2]}

['no overall', 'no in series', 'title', 'director', 'writer', 'original air date', 'production code']

[['32', '1', 'episode 1', 'barnaby southcomb', 'richard stoneman', '26 october 2004', '401'], ['33', '2', 'episode 2', 'barnaby southcomb', 'ed roe', '3 november 2004', '402'], ['34', '3', 'episode 3', 'barnaby southcomb', 'charlie martin', '10 november 2004', '403'], ['35', '4', 'episode 4', 'sean grundy', 'linton chiswick', '17 november 2004', '404'], ['36', '5', 'episode 5', 'sean grundy', 'jack lothian', '24 november 2004', '405'], ['37', '6', 'episode 6', 'sean grundy', 'tony basgallop', '1 december 2004', '406'], ['38', '7', 'episode 7', 'iain b macdonald', 'charlie martin', '8 december 2004', '407'], ['39', '8', 'episode 8', 'iain b macdonald', 'richard stoneman', '15 december 2004', '408']]