niryuu/sni-each-converted huggingface dataset:
Command: eq { count { filter_eq { all_rows ; time ; +40 } } ; 2 }, interpretation: select the rows whose margin record fuzzily matches to 8 strokes . there is only one such row in the table . the championship record of this unqiue row is us women 's open .
Output: no
Command: and { only { filter_eq { all_rows ; margin ; 8 strokes } } ; eq { hop { filter_eq { all_rows ; margin ; 8 strokes } ; championship } ; us women 's open } }, interpretation: select the rows whose margin record fuzzily matches to 8 strokes . there is only one such row in the table . the championship record of this unqiue row is us women 's open .
Output: yes
Command: and { only { filter_eq { all_rows ; margin ; 8 strokes } } ; eq { hop { filter_eq { all_rows ; margin ; 8 strokes } ; championship } ; us women 's open } }, interpretation: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is geelong . the away team record of this unqiue row is north melbourne . the venue record of this unqiue row is kardinia park .
Output: no
Command: and { only { filter_less { all_rows ; crowd ; 10000 } } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; geelong } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; away team } ; north melbourne } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; venue } ; kardinia park } } } }, interpretation: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is geelong . the away team record of this unqiue row is north melbourne . the venue record of this unqiue row is kardinia park .
Output: yes
Command: and { only { filter_less { all_rows ; crowd ; 10000 } } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; geelong } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; away team } ; north melbourne } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; venue } ; kardinia park } } } }, interpretation: select the rows whose 2011 gdp ( ppp ) billions of usd record is less than 100 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_less { all_rows ; 2011 gdp ( ppp ) billions of usd ; 100 } } ; 5 }, interpretation: select the rows whose 2011 gdp ( ppp ) billions of usd record is less than 100 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_less { all_rows ; 2011 gdp ( ppp ) billions of usd ; 100 } } ; 5 }, interpretation: for the year records of all rows , most of them are less than 2000 .
Output: no
Command: most_less { all_rows ; year ; 2000 }, interpretation: for the year records of all rows , most of them are less than 2000 .
Output: yes
Command: most_less { all_rows ; year ; 2000 }, interpretation: the 1st minimum round record of all rows is 1 . the player record of the row with 1st minimum round record is john hummer .
Output: no
Command: and { eq { nth_min { all_rows ; round ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; round ; 1 } ; player } ; john hummer } }, interpretation: the 1st minimum round record of all rows is 1 . the player record of the row with 1st minimum round record is john hummer .
Output: yes
Command: and { eq { nth_min { all_rows ; round ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; round ; 1 } ; player } ; john hummer } }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is january 19 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; january 19 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is january 19 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; january 19 }, interpretation: select the rows whose school ( ihsaa id ) record fuzzily matches to knox community . take the enrollment record of this row . select the rows whose school ( ihsaa id ) record fuzzily matches to culver community . take the enrollment record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; school ( ihsaa id ) ; knox community } ; enrollment } ; hop { filter_eq { all_rows ; school ( ihsaa id ) ; culver community } ; enrollment } }, interpretation: select the rows whose school ( ihsaa id ) record fuzzily matches to knox community . take the enrollment record of this row . select the rows whose school ( ihsaa id ) record fuzzily matches to culver community . take the enrollment record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; school ( ihsaa id ) ; knox community } ; enrollment } ; hop { filter_eq { all_rows ; school ( ihsaa id ) ; culver community } ; enrollment } }, interpretation: for the champions records of all rows , most of them fuzzily match to sparta prague .
Output: no
Command: most_eq { all_rows ; champions ; sparta prague }, interpretation: for the champions records of all rows , most of them fuzzily match to sparta prague .
Output: yes
Command: most_eq { all_rows ; champions ; sparta prague }, interpretation: select the rows whose event record fuzzily matches to wimbledon . select the row whose aces record of these rows is 1st maximum . the player record of this row is john isner .
Output: no
Command: eq { hop { nth_argmax { filter_eq { all_rows ; event ; wimbledon } ; aces ; 1 } ; player } ; john isner }, interpretation: select the rows whose event record fuzzily matches to wimbledon . select the row whose aces record of these rows is 1st maximum . the player record of this row is john isner .
Output: yes
Command: eq { hop { nth_argmax { filter_eq { all_rows ; event ; wimbledon } ; aces ; 1 } ; player } ; john isner }, interpretation: select the rows whose finish record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1972 .
Output: no
Command: and { only { filter_eq { all_rows ; finish ; 10 } } ; eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 } }, interpretation: select the rows whose finish record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1972 .
Output: yes
Command: and { only { filter_eq { all_rows ; finish ; 10 } } ; eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 } }, interpretation: the average of the total record of all rows is 152.8 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 152.8 }, interpretation: the average of the total record of all rows is 152.8 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 152.8 }, interpretation: select the row whose date record of all rows is 1st minimum . the championship record of this row is merion , us .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; championship } ; merion , us }, interpretation: select the row whose date record of all rows is 1st minimum . the championship record of this row is merion , us .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; championship } ; merion , us }, interpretation: select the rows whose place record fuzzily matches to 3rd . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; place ; 3rd } } ; 5 }, interpretation: select the rows whose place record fuzzily matches to 3rd . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; place ; 3rd } } ; 5 }, interpretation: select the rows whose rushing yards record is less than 1000 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: no
Command: and { only { filter_less { all_rows ; rushing yards ; 1000 } } ; eq { hop { filter_less { all_rows ; rushing yards ; 1000 } ; year } ; 1989 } }, interpretation: select the rows whose rushing yards record is less than 1000 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: yes
Command: and { only { filter_less { all_rows ; rushing yards ; 1000 } } ; eq { hop { filter_less { all_rows ; rushing yards ; 1000 } ; year } ; 1989 } }, interpretation: for the label records of all rows , most of them fuzzily match to parlophone .
Output: no
Command: most_eq { all_rows ; label ; parlophone }, interpretation: for the label records of all rows , most of them fuzzily match to parlophone .
Output: yes
Command: most_eq { all_rows ; label ; parlophone }, interpretation: select the rows whose first elected record is less than 1940 . among these rows , select the rows whose result record fuzzily matches to elected . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_less { all_rows ; first elected ; 1940 } ; result ; elected } } ; 2 }, interpretation: select the rows whose first elected record is less than 1940 . among these rows , select the rows whose result record fuzzily matches to elected . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_less { all_rows ; first elected ; 1940 } ; result ; elected } } ; 2 }, interpretation: select the rows whose relationship record fuzzily matches to mac taylor . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; relationship ; mac taylor } } ; 2 }, interpretation: select the rows whose relationship record fuzzily matches to mac taylor . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; relationship ; mac taylor } } ; 2 }, interpretation: select the rows whose rank record is less than or equal to 10 . the number of such rows is 8 .
Output: no
Command: eq { count { filter_less_eq { all_rows ; rank ; 10 } } ; 8 }, interpretation: select the rows whose rank record is less than or equal to 10 . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_less_eq { all_rows ; rank ; 10 } } ; 8 }, interpretation: for the location records of all rows , most of them fuzzily match to bayamón , puerto rico .
Output: no
Command: most_eq { all_rows ; location ; bayamón , puerto rico }, interpretation: for the location records of all rows , most of them fuzzily match to bayamón , puerto rico .
Output: yes
Command: most_eq { all_rows ; location ; bayamón , puerto rico }, interpretation: select the row whose population record of all rows is maximum . the english name record of this row is luobei county .
Output: no
Command: eq { hop { argmax { all_rows ; population } ; english name } ; luobei county }, interpretation: select the row whose population record of all rows is maximum . the english name record of this row is luobei county .
Output: yes
Command: eq { hop { argmax { all_rows ; population } ; english name } ; luobei county }, interpretation: for the date records of all rows , all of them fuzzily match to january .
Output: no
Command: all_eq { all_rows ; date ; january }, interpretation: for the date records of all rows , all of them fuzzily match to january .
Output: yes
Command: all_eq { all_rows ; date ; january }, interpretation: select the rows whose opposing teams record fuzzily matches to new zealand . the sum of the against record of these rows is 18 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against } ; 18 }, interpretation: select the rows whose opposing teams record fuzzily matches to new zealand . the sum of the against record of these rows is 18 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against } ; 18 }, interpretation: select the rows whose name record fuzzily matches to kevin barnett . take the weight record of this row . select the rows whose name record fuzzily matches to gabriel gardner . take the weight record of this row . the second record is 9 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; kevin barnett } ; weight } ; hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight } } ; -9 }, interpretation: select the rows whose name record fuzzily matches to kevin barnett . take the weight record of this row . select the rows whose name record fuzzily matches to gabriel gardner . take the weight record of this row . the second record is 9 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; kevin barnett } ; weight } ; hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight } } ; -9 }, interpretation: select the rows whose w / l record fuzzily matches to win . the number of such rows is 9 .
Output: no
Command: eq { count { filter_eq { all_rows ; w / l ; win } } ; 9 }, interpretation: select the rows whose w / l record fuzzily matches to win . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_eq { all_rows ; w / l ; win } } ; 9 }, interpretation: select the row whose year record of all rows is 2nd minimum . the series title record of this row is dragon laws ii : kidnapped .
Output: no
Command: eq { hop { nth_argmin { all_rows ; year ; 2 } ; series title } ; dragon laws ii : kidnapped }, interpretation: select the row whose year record of all rows is 2nd minimum . the series title record of this row is dragon laws ii : kidnapped .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; year ; 2 } ; series title } ; dragon laws ii : kidnapped }, interpretation: select the rows whose engine record fuzzily matches to novi v8 . there is only one such row in the table . the year record of this unqiue row is 1956 .
Output: no
Command: and { only { filter_eq { all_rows ; engine ; novi v8 } } ; eq { hop { filter_eq { all_rows ; engine ; novi v8 } ; year } ; 1956 } }, interpretation: select the rows whose engine record fuzzily matches to novi v8 . there is only one such row in the table . the year record of this unqiue row is 1956 .
Output: yes
Command: and { only { filter_eq { all_rows ; engine ; novi v8 } } ; eq { hop { filter_eq { all_rows ; engine ; novi v8 } ; year } ; 1956 } }, interpretation: the 2nd minimum year record of all rows is 1980 .
Output: no
Command: eq { nth_min { all_rows ; year ; 2 } ; 1980 }, interpretation: the 2nd minimum year record of all rows is 1980 .
Output: yes
Command: eq { nth_min { all_rows ; year ; 2 } ; 1980 }, interpretation: select the row whose year record of all rows is minimum . the english title record of this row is demon skyscraper .
Output: no
Command: eq { hop { argmin { all_rows ; year } ; english title } ; demon skyscraper }, interpretation: select the row whose year record of all rows is minimum . the english title record of this row is demon skyscraper .
Output: yes
Command: eq { hop { argmin { all_rows ; year } ; english title } ; demon skyscraper }, interpretation: select the row whose margin of victory record of all rows is maximum . the date record of this row is 22 jun 2006 .
Output: no
Command: eq { hop { argmax { all_rows ; margin of victory } ; date } ; 22 jun 2006 }, interpretation: select the row whose margin of victory record of all rows is maximum . the date record of this row is 22 jun 2006 .
Output: yes
Command: eq { hop { argmax { all_rows ; margin of victory } ; date } ; 22 jun 2006 }, interpretation: select the rows whose gender record fuzzily matches to f . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; gender ; f } } ; 2 }, interpretation: select the rows whose gender record fuzzily matches to f . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; gender ; f } } ; 2 }, interpretation: select the row whose purse record of all rows is maximum . the tournament record of this row is senior tournament players championship .
Output: no
Command: eq { hop { argmax { all_rows ; purse } ; tournament } ; senior tournament players championship }, interpretation: select the row whose purse record of all rows is maximum . the tournament record of this row is senior tournament players championship .
Output: yes
Command: eq { hop { argmax { all_rows ; purse } ; tournament } ; senior tournament players championship }, interpretation: select the row whose silver record of all rows is maximum . the nation record of this row is netherlands ( ned ) .
Output: no
Command: eq { hop { argmax { all_rows ; silver } ; nation } ; netherlands ( ned ) }, interpretation: select the row whose silver record of all rows is maximum . the nation record of this row is netherlands ( ned ) .
Output: yes
Command: eq { hop { argmax { all_rows ; silver } ; nation } ; netherlands ( ned ) }, interpretation: select the rows whose played record fuzzily matches to 30 . the number of such rows is 16 .
Output: no
Command: eq { count { filter_eq { all_rows ; played ; 30 } } ; 16 }, interpretation: select the rows whose played record fuzzily matches to 30 . the number of such rows is 16 .
Output: yes
Command: eq { count { filter_eq { all_rows ; played ; 30 } } ; 16 }, interpretation: select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table . the title record of this unqiue row is long days journey into stan .
Output: no
Command: and { only { filter_eq { all_rows ; written by ; danielle evenson } } ; eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan } }, interpretation: select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table . the title record of this unqiue row is long days journey into stan .
Output: yes
Command: and { only { filter_eq { all_rows ; written by ; danielle evenson } } ; eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan } }, interpretation: the average of the attendance record of all rows is 24652 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 24652 }, interpretation: the average of the attendance record of all rows is 24652 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 24652 }, interpretation: select the rows whose commanding officer record fuzzily matches to n/a . there is only one such row in the table . the parent unit record of this unqiue row is jagdgeschwader 54 .
Output: no
Command: and { only { filter_eq { all_rows ; commanding officer ; n/a } } ; eq { hop { filter_eq { all_rows ; commanding officer ; n/a } ; parent unit } ; jagdgeschwader 54 } }, interpretation: select the rows whose commanding officer record fuzzily matches to n/a . there is only one such row in the table . the parent unit record of this unqiue row is jagdgeschwader 54 .
Output: yes
Command: and { only { filter_eq { all_rows ; commanding officer ; n/a } } ; eq { hop { filter_eq { all_rows ; commanding officer ; n/a } ; parent unit } ; jagdgeschwader 54 } }, interpretation: select the rows whose owned since record is equal to 2011 . the number of such rows is 9 .
Output: no
Command: eq { count { filter_eq { all_rows ; owned since ; 2011 } } ; 9 }, interpretation: select the rows whose owned since record is equal to 2011 . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_eq { all_rows ; owned since ; 2011 } } ; 9 }, interpretation: for the wins records of all rows , all of them are equal to 0 .
Output: no
Command: all_eq { all_rows ; wins ; 0 }, interpretation: for the wins records of all rows , all of them are equal to 0 .
Output: yes
Command: all_eq { all_rows ; wins ; 0 }, interpretation: select the rows whose reason for change record fuzzily matches to to enter the us army . there is only one such row in the table . the vacator record of this unqiue row is charles i faddis ( d ) .
Output: no
Command: and { only { filter_eq { all_rows ; reason for change ; to enter the us army } } ; eq { hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator } ; charles i faddis ( d ) } }, interpretation: select the rows whose reason for change record fuzzily matches to to enter the us army . there is only one such row in the table . the vacator record of this unqiue row is charles i faddis ( d ) .
Output: yes
Command: and { only { filter_eq { all_rows ; reason for change ; to enter the us army } } ; eq { hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator } ; charles i faddis ( d ) } }, interpretation: for the result records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; result ; l }, interpretation: for the result records of all rows , most of them fuzzily match to l .
Output: yes
Command: most_eq { all_rows ; result ; l }, interpretation: for the play - off records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; play - off ; 0 }, interpretation: for the play - off records of all rows , most of them are equal to 0 .
Output: yes
Command: most_eq { all_rows ; play - off ; 0 }, interpretation: the average of the effective exhaust velocity ( m / s ) record of all rows is 33869 .
Output: no
Command: round_eq { avg { all_rows ; effective exhaust velocity ( m / s ) } ; 33869 }, interpretation: the average of the effective exhaust velocity ( m / s ) record of all rows is 33869 .
Output: yes
Command: round_eq { avg { all_rows ; effective exhaust velocity ( m / s ) } ; 33869 }, interpretation: select the rows whose name record fuzzily matches to keutenberg . take the kilometer record of this row . select the rows whose name record fuzzily matches to fromberg . take the kilometer record of this row . the first record is 4 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; keutenberg } ; kilometer } ; hop { filter_eq { all_rows ; name ; fromberg } ; kilometer } } ; 4 }, interpretation: select the rows whose name record fuzzily matches to keutenberg . take the kilometer record of this row . select the rows whose name record fuzzily matches to fromberg . take the kilometer record of this row . the first record is 4 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; keutenberg } ; kilometer } ; hop { filter_eq { all_rows ; name ; fromberg } ; kilometer } } ; 4 }, interpretation: select the row whose to par record of all rows is maximum . the player record of this row is tiger woods .
Output: no
Command: eq { hop { argmax { all_rows ; to par } ; player } ; tiger woods }, interpretation: select the row whose to par record of all rows is maximum . the player record of this row is tiger woods .
Output: yes
Command: eq { hop { argmax { all_rows ; to par } ; player } ; tiger woods }, interpretation: the 2nd maximum weeks record of all rows is 16 weeks .
Output: no
Command: eq { nth_max { all_rows ; weeks ; 2 } ; 16 weeks }, interpretation: the 2nd maximum weeks record of all rows is 16 weeks .
Output: yes
Command: eq { nth_max { all_rows ; weeks ; 2 } ; 16 weeks }, interpretation: select the rows whose segment b record fuzzily matches to cheesecake . there is only one such row in the table . the series ep record of this unqiue row is 9 - 01 .
Output: no
Command: and { only { filter_eq { all_rows ; segment b ; cheesecake } } ; eq { hop { filter_eq { all_rows ; segment b ; cheesecake } ; series ep } ; 9 - 01 } }, interpretation: select the rows whose segment b record fuzzily matches to cheesecake . there is only one such row in the table . the series ep record of this unqiue row is 9 - 01 .
Output: yes
Command: and { only { filter_eq { all_rows ; segment b ; cheesecake } } ; eq { hop { filter_eq { all_rows ; segment b ; cheesecake } ; series ep } ; 9 - 01 } }, interpretation: select the rows whose incumbent record fuzzily matches to james m ashley . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to samuel shellabarger . take the first elected record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; incumbent ; james m ashley } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; samuel shellabarger } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to james m ashley . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to samuel shellabarger . take the first elected record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; incumbent ; james m ashley } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; samuel shellabarger } ; first elected } }, interpretation: for the year records of all rows , most of them are greater than 2000 .
Output: no
Command: most_greater { all_rows ; year ; 2000 }, interpretation: for the year records of all rows , most of them are greater than 2000 .
Output: yes
Command: most_greater { all_rows ; year ; 2000 }, interpretation: select the rows whose name record fuzzily matches to amangul mollayeva . take the ties record of this row . select the rows whose name record fuzzily matches to ayna ereshova . take the ties record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; amangul mollayeva } ; ties } ; hop { filter_eq { all_rows ; name ; ayna ereshova } ; ties } }, interpretation: select the rows whose name record fuzzily matches to amangul mollayeva . take the ties record of this row . select the rows whose name record fuzzily matches to ayna ereshova . take the ties record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; amangul mollayeva } ; ties } ; hop { filter_eq { all_rows ; name ; ayna ereshova } ; ties } }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 9 .
Output: no
Command: eq { count { filter_all { all_rows ; nation } } ; 9 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_all { all_rows ; nation } } ; 9 }, interpretation: select the row whose total viewers ( in millions ) record of all rows is maximum . the title record of this row is our fathers .
Output: no
Command: eq { hop { argmax { all_rows ; total viewers ( in millions ) } ; title } ; our fathers }, interpretation: select the row whose total viewers ( in millions ) record of all rows is maximum . the title record of this row is our fathers .
Output: yes
Command: eq { hop { argmax { all_rows ; total viewers ( in millions ) } ; title } ; our fathers }, interpretation: select the rows whose west lancashire record is equal to 4 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; west lancashire ; 4 } } ; 2 }, interpretation: select the rows whose west lancashire record is equal to 4 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; west lancashire ; 4 } } ; 2 }, interpretation: select the row whose balls record of all rows is maximum . the player record of this row is keith boyce .
Output: no
Command: eq { hop { argmax { all_rows ; balls } ; player } ; keith boyce }, interpretation: select the row whose balls record of all rows is maximum . the player record of this row is keith boyce .
Output: yes
Command: eq { hop { argmax { all_rows ; balls } ; player } ; keith boyce }, interpretation: select the row whose votes record of all rows is 3rd maximum . the candidate 's name record of this row is michael mackay .
Output: no
Command: eq { hop { nth_argmax { all_rows ; votes ; 3 } ; candidate 's name } ; michael mackay }, interpretation: select the row whose votes record of all rows is 3rd maximum . the candidate 's name record of this row is michael mackay .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; votes ; 3 } ; candidate 's name } ; michael mackay }, interpretation: select the rows whose time record is greater than 48 . there is only one such row in the table . the athlete record of this unqiue row is naiel santiago d'almeida .
Output: no
Command: and { only { filter_greater { all_rows ; time ; 48 } } ; eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida } }, interpretation: select the rows whose time record is greater than 48 . there is only one such row in the table . the athlete record of this unqiue row is naiel santiago d'almeida .
Output: yes
Command: and { only { filter_greater { all_rows ; time ; 48 } } ; eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida } }, interpretation: for the soap opera records of all rows , most of them fuzzily match to un posto al sole .
Output: no
Command: most_eq { all_rows ; soap opera ; un posto al sole }, interpretation: for the soap opera records of all rows , most of them fuzzily match to un posto al sole .
Output: yes
Command: most_eq { all_rows ; soap opera ; un posto al sole }, interpretation: the average of the population record of all rows is 365649.3 .
Output: no
Command: round_eq { avg { all_rows ; population } ; 365649.3 }, interpretation: the average of the population record of all rows is 365649.3 .
Output: yes
Command: round_eq { avg { all_rows ; population } ; 365649.3 }, interpretation: select the rows whose championship record fuzzily matches to wimbledon . select the row whose year record of these rows is minimum . the opponent in the final record of this row is wilhelm bungert .
Output: no
Command: eq { hop { argmin { filter_eq { all_rows ; championship ; wimbledon } ; year } ; opponent in the final } ; wilhelm bungert }, interpretation: select the rows whose championship record fuzzily matches to wimbledon . select the row whose year record of these rows is minimum . the opponent in the final record of this row is wilhelm bungert .
Output: yes
Command: eq { hop { argmin { filter_eq { all_rows ; championship ; wimbledon } ; year } ; opponent in the final } ; wilhelm bungert }, interpretation: select the rows whose incumbent record fuzzily matches to doug walgren . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to tom ridge . take the first elected record of this row . the second record is 6 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; doug walgren } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; tom ridge } ; first elected } } ; -6 }, interpretation: select the rows whose incumbent record fuzzily matches to doug walgren . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to tom ridge . take the first elected record of this row . the second record is 6 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; doug walgren } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; tom ridge } ; first elected } } ; -6 }, interpretation: select the rows whose segment c record fuzzily matches to part 1 . there is only one such row in the table . the episode record of this unqiue row is 147 .
Output: no
Command: and { only { filter_eq { all_rows ; segment c ; part 1 } } ; eq { hop { filter_eq { all_rows ; segment c ; part 1 } ; episode } ; 147 } }, interpretation: select the rows whose segment c record fuzzily matches to part 1 . there is only one such row in the table . the episode record of this unqiue row is 147 .
Output: yes
Command: and { only { filter_eq { all_rows ; segment c ; part 1 } } ; eq { hop { filter_eq { all_rows ; segment c ; part 1 } ; episode } ; 147 } }, interpretation: for the high points records of all rows , most of them fuzzily match to eric gordon .
Output: no
Command: most_eq { all_rows ; high points ; eric gordon }, interpretation: for the high points records of all rows , most of them fuzzily match to eric gordon .
Output: yes
Command: most_eq { all_rows ; high points ; eric gordon }, interpretation: select the rows whose player record fuzzily matches to b lee . take the wkts record of this row . select the rows whose player record fuzzily matches to z khan . take the wkts record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; b lee } ; wkts } ; hop { filter_eq { all_rows ; player ; z khan } ; wkts } }, interpretation: select the rows whose player record fuzzily matches to b lee . take the wkts record of this row . select the rows whose player record fuzzily matches to z khan . take the wkts record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; b lee } ; wkts } ; hop { filter_eq { all_rows ; player ; z khan } ; wkts } }, interpretation: select the row whose league record of all rows is 3rd maximum . the name record of this row is bill foulkes .
Output: no
Command: eq { hop { nth_argmax { all_rows ; league ; 3 } ; name } ; bill foulkes }, interpretation: select the row whose league record of all rows is 3rd maximum . the name record of this row is bill foulkes .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; league ; 3 } ; name } ; bill foulkes }, interpretation: select the rows whose year born record is equal to 1986 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; year born ; 1986 } } ; 2 }, interpretation: select the rows whose year born record is equal to 1986 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; year born ; 1986 } } ; 2 }, interpretation: the average of the score record of all rows is 277 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 277 }, interpretation: the average of the score record of all rows is 277 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 277 }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park }, interpretation: select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table .
Output: no
Command: only { filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l } }, interpretation: select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table .
Output: yes
Command: only { filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l } }, interpretation: select the row whose date record of all rows is 2nd maximum . the opponent in the final record of this row is katerina maleeva .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent in the final } ; katerina maleeva }, interpretation: select the row whose date record of all rows is 2nd maximum . the opponent in the final record of this row is katerina maleeva .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent in the final } ; katerina maleeva }, interpretation: for the result records of all rows , all of them fuzzily match to re-elected .
Output: no
Command: all_eq { all_rows ; result ; re-elected }, interpretation: for the result records of all rows , all of them fuzzily match to re-elected .
Output: yes
Command: all_eq { all_rows ; result ; re-elected }, interpretation: select the rows whose losses record is equal to 9 . there is only one such row in the table . the club record of this unqiue row is sestao .
Output: no
Command: and { only { filter_eq { all_rows ; losses ; 9 } } ; eq { hop { filter_eq { all_rows ; losses ; 9 } ; club } ; sestao } }, interpretation: select the rows whose losses record is equal to 9 . there is only one such row in the table . the club record of this unqiue row is sestao .
Output: yes
Command: and { only { filter_eq { all_rows ; losses ; 9 } } ; eq { hop { filter_eq { all_rows ; losses ; 9 } ; club } ; sestao } }, interpretation: select the rows whose county record fuzzily matches to putnam . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; county ; putnam } } ; 4 }, interpretation: select the rows whose county record fuzzily matches to putnam . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; county ; putnam } } ; 4 }, interpretation: select the rows whose tournament record fuzzily matches to indianapolis . take the date record of this row . select the rows whose tournament record fuzzily matches to palm beach gardens . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; tournament ; indianapolis } ; date } ; hop { filter_eq { all_rows ; tournament ; palm beach gardens } ; date } }, interpretation: select the rows whose tournament record fuzzily matches to indianapolis . take the date record of this row . select the rows whose tournament record fuzzily matches to palm beach gardens . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; tournament ; indianapolis } ; date } ; hop { filter_eq { all_rows ; tournament ; palm beach gardens } ; date } }, interpretation: select the row whose pts record of all rows is 3rd maximum . the season record of this row is 2010 - 11 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; pts ; 3 } ; season } ; 2010 - 11 }, interpretation: select the row whose pts record of all rows is 3rd maximum . the season record of this row is 2010 - 11 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; pts ; 3 } ; season } ; 2010 - 11 }, interpretation: select the rows whose days with frost record is equal to 0 . there is only one such row in the table . the city / town record of this unqiue row is a coruña .
Output: no
Command: and { only { filter_eq { all_rows ; days with frost ; 0 } } ; eq { hop { filter_eq { all_rows ; days with frost ; 0 } ; city / town } ; a coruña } }, interpretation: select the rows whose days with frost record is equal to 0 . there is only one such row in the table . the city / town record of this unqiue row is a coruña .
Output: yes
Command: and { only { filter_eq { all_rows ; days with frost ; 0 } } ; eq { hop { filter_eq { all_rows ; days with frost ; 0 } ; city / town } ; a coruña } }, interpretation: select the rows whose opponent record fuzzily matches to east fife . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; opponent ; east fife } } ; 2 }, interpretation: select the rows whose opponent record fuzzily matches to east fife . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; opponent ; east fife } } ; 2 }, interpretation: select the rows whose time / retired record fuzzily matches to engine . there is only one such row in the table . the driver record of this unqiue row is jo bonnier .
Output: no
Command: and { only { filter_eq { all_rows ; time / retired ; engine } } ; eq { hop { filter_eq { all_rows ; time / retired ; engine } ; driver } ; jo bonnier } }, interpretation: select the rows whose time / retired record fuzzily matches to engine . there is only one such row in the table . the driver record of this unqiue row is jo bonnier .
Output: yes
Command: and { only { filter_eq { all_rows ; time / retired ; engine } } ; eq { hop { filter_eq { all_rows ; time / retired ; engine } ; driver } ; jo bonnier } }, interpretation: the average of the points record of all rows is 325.4 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 325.4 }, interpretation: the average of the points record of all rows is 325.4 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 325.4 }, interpretation: select the rows whose team record fuzzily matches to oklahoma city . select the row whose date record of these rows is 2nd minimum . the location attendance record of this row is toyota center 15316 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 } ; location attendance } ; toyota center 15316 }, interpretation: select the rows whose team record fuzzily matches to oklahoma city . select the row whose date record of these rows is 2nd minimum . the location attendance record of this row is toyota center 15316 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 } ; location attendance } ; toyota center 15316 }, interpretation: select the rows whose affiliation record fuzzily matches to private/catholic . the average of the enrollment record of these rows is 3,847 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; affiliation ; private/catholic } ; enrollment } ; 3,847 }, interpretation: select the rows whose affiliation record fuzzily matches to private/catholic . the average of the enrollment record of these rows is 3,847 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; affiliation ; private/catholic } ; enrollment } ; 3,847 }, interpretation: for the high assists records of all rows , most of them fuzzily match to lebron james .
Output: no
Command: most_eq { all_rows ; high assists ; lebron james }, interpretation: for the high assists records of all rows , most of them fuzzily match to lebron james .
Output: yes
Command: most_eq { all_rows ; high assists ; lebron james }, interpretation: the average of the crowd record of all rows is 18729 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 18729 }, interpretation: the average of the crowd record of all rows is 18729 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 18729 }, interpretation: select the rows whose player record fuzzily matches to kenny solomon . take the td 's record of this row . select the rows whose player record fuzzily matches to chris ryan . take the td 's record of this row . the first record is 2 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; player ; kenny solomon } ; td 's } ; hop { filter_eq { all_rows ; player ; chris ryan } ; td 's } } ; 2 }, interpretation: select the rows whose player record fuzzily matches to kenny solomon . take the td 's record of this row . select the rows whose player record fuzzily matches to chris ryan . take the td 's record of this row . the first record is 2 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; player ; kenny solomon } ; td 's } ; hop { filter_eq { all_rows ; player ; chris ryan } ; td 's } } ; 2 }, interpretation: the average of the crowd record of all rows is 16000 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 16000 }, interpretation: the average of the crowd record of all rows is 16000 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 16000 }, interpretation: select the rows whose pole position record fuzzily matches to mattias ekström . take the date record of this row . select the rows whose pole position record fuzzily matches to oliver jarvis . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; pole position ; mattias ekström } ; date } ; hop { filter_eq { all_rows ; pole position ; oliver jarvis } ; date } }, interpretation: select the rows whose pole position record fuzzily matches to mattias ekström . take the date record of this row . select the rows whose pole position record fuzzily matches to oliver jarvis . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; pole position ; mattias ekström } ; date } ; hop { filter_eq { all_rows ; pole position ; oliver jarvis } ; date } }, interpretation: select the row whose growth rate record of all rows is minimum . the district record of this row is kolkata .
Output: no
Command: eq { hop { argmin { all_rows ; growth rate } ; district } ; kolkata }, interpretation: select the row whose growth rate record of all rows is minimum . the district record of this row is kolkata .
Output: yes
Command: eq { hop { argmin { all_rows ; growth rate } ; district } ; kolkata }, interpretation: select the rows whose skipper record fuzzily matches to andy forbes . take the combined elapsed time record of this row . select the rows whose skipper record fuzzily matches to stuart jackson . take the combined elapsed time record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; skipper ; andy forbes } ; combined elapsed time } ; hop { filter_eq { all_rows ; skipper ; stuart jackson } ; combined elapsed time } }, interpretation: select the rows whose skipper record fuzzily matches to andy forbes . take the combined elapsed time record of this row . select the rows whose skipper record fuzzily matches to stuart jackson . take the combined elapsed time record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; skipper ; andy forbes } ; combined elapsed time } ; hop { filter_eq { all_rows ; skipper ; stuart jackson } ; combined elapsed time } }, interpretation: select the rows whose title record fuzzily matches to mount rushmore . take the original air date record of this row . select the rows whose title record fuzzily matches to salem . take the original air date record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; title ; mount rushmore } ; original air date } ; hop { filter_eq { all_rows ; title ; salem } ; original air date } }, interpretation: select the rows whose title record fuzzily matches to mount rushmore . take the original air date record of this row . select the rows whose title record fuzzily matches to salem . take the original air date record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; title ; mount rushmore } ; original air date } ; hop { filter_eq { all_rows ; title ; salem } ; original air date } }, interpretation: select the rows whose sydney record fuzzily matches to yes . the number of such rows is 13 .
Output: no
Command: eq { count { filter_eq { all_rows ; sydney ; yes } } ; 13 }, interpretation: select the rows whose sydney record fuzzily matches to yes . the number of such rows is 13 .
Output: yes
Command: eq { count { filter_eq { all_rows ; sydney ; yes } } ; 13 }, interpretation: select the rows whose candidates record is greater than or equal to 55.0 % . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater_eq { all_rows ; candidates ; 55.0 % } } ; 2 }, interpretation: select the rows whose candidates record is greater than or equal to 55.0 % . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater_eq { all_rows ; candidates ; 55.0 % } } ; 2 }, interpretation: the sum of the population ( 2008 ) record of all rows is 7062211 .
Output: no
Command: round_eq { sum { all_rows ; population ( 2008 ) } ; 7062211 }, interpretation: the sum of the population ( 2008 ) record of all rows is 7062211 .
Output: yes
Command: round_eq { sum { all_rows ; population ( 2008 ) } ; 7062211 }, interpretation: for the days records of all rows , most of them are equal to 99 .
Output: no
Command: most_eq { all_rows ; days ; 99 }, interpretation: for the days records of all rows , most of them are equal to 99 .
Output: yes
Command: most_eq { all_rows ; days ; 99 }, interpretation: select the rows whose competition record fuzzily matches to 1978 merdeka cup . take the score record of this row . select the rows whose competition record fuzzily matches to 1986 fifa world cup . take the score record of this row . the first record fuzzily matches to the second record .
Output: no