niryuu/sni-each-converted huggingface dataset:
Command: round_eq { avg { all_rows ; pick } ; 16.6 }, interpretation: the average of the pick record of all rows is 16.6 .
Output: yes
Command: round_eq { avg { all_rows ; pick } ; 16.6 }, interpretation: select the rows whose builder record fuzzily matches to general dynamics , quincy . select the row whose commissioned - decommissioned record of these rows is 1st minimum . the ship record of this row is wichita .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; builder ; general dynamics , quincy } ; commissioned - decommissioned ; 1 } ; ship } ; wichita }, interpretation: select the rows whose builder record fuzzily matches to general dynamics , quincy . select the row whose commissioned - decommissioned record of these rows is 1st minimum . the ship record of this row is wichita .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; builder ; general dynamics , quincy } ; commissioned - decommissioned ; 1 } ; ship } ; wichita }, interpretation: select the rows whose player record fuzzily matches to mitchell johnson . take the innings record of this row . select the rows whose player record fuzzily matches to shane lee . take the innings record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; mitchell johnson } ; innings } ; hop { filter_eq { all_rows ; player ; shane lee } ; innings } }, interpretation: select the rows whose player record fuzzily matches to mitchell johnson . take the innings record of this row . select the rows whose player record fuzzily matches to shane lee . take the innings record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; mitchell johnson } ; innings } ; hop { filter_eq { all_rows ; player ; shane lee } ; innings } }, interpretation: select the row whose pick record of all rows is 4th minimum . the player record of this row is marcus harrison .
Output: no
Command: eq { hop { nth_argmin { all_rows ; pick ; 4 } ; player } ; marcus harrison }, interpretation: select the row whose pick record of all rows is 4th minimum . the player record of this row is marcus harrison .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; pick ; 4 } ; player } ; marcus harrison }, interpretation: select the row whose season record of all rows is 5th minimum . the team record of this row is runcorn highfield .
Output: no
Command: eq { hop { nth_argmin { all_rows ; season ; 5 } ; team } ; runcorn highfield }, interpretation: select the row whose season record of all rows is 5th minimum . the team record of this row is runcorn highfield .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; season ; 5 } ; team } ; runcorn highfield }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is less than 140000 . there is only one such row in the table .
Output: no
Command: only { filter_less { filter_eq { all_rows ; country ; united states } ; money ; 140000 } }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is less than 140000 . there is only one such row in the table .
Output: yes
Command: only { filter_less { filter_eq { all_rows ; country ; united states } ; money ; 140000 } }, interpretation: select the rows whose position record fuzzily matches to running back . for the round records of these rows , all of them are less than 10 .
Output: no
Command: all_less { filter_eq { all_rows ; position ; running back } ; round ; 10 }, interpretation: select the rows whose position record fuzzily matches to running back . for the round records of these rows , all of them are less than 10 .
Output: yes
Command: all_less { filter_eq { all_rows ; position ; running back } ; round ; 10 }, interpretation: select the row whose last match record of all rows is maximum . the competition record of this row is uefa champions league .
Output: no
Command: eq { hop { argmax { all_rows ; last match } ; competition } ; uefa champions league }, interpretation: select the row whose last match record of all rows is maximum . the competition record of this row is uefa champions league .
Output: yes
Command: eq { hop { argmax { all_rows ; last match } ; competition } ; uefa champions league }, interpretation: for the displacement records of all rows , most of them are equal to 3.9 .
Output: no
Command: most_eq { all_rows ; displacement ; 3.9 }, interpretation: for the displacement records of all rows , most of them are equal to 3.9 .
Output: yes
Command: most_eq { all_rows ; displacement ; 3.9 }, interpretation: the average of the attendance record of all rows is 17863 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 17863 }, interpretation: the average of the attendance record of all rows is 17863 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 17863 }, interpretation: select the rows whose week 5 record fuzzily matches to melisa jackson . take the record of this row . select the rows whose week 5 record fuzzily matches to - . take the record of this row . the first record is less than the second record . the record of the first row is january . the record of the second row is february .
Output: no
Command: and { less { hop { filter_eq { all_rows ; week 5 ; melisa jackson } ; } ; hop { filter_eq { all_rows ; week 5 ; - } ; } } ; and { eq { hop { filter_eq { all_rows ; week 5 ; melisa jackson } ; } ; january } ; eq { hop { filter_eq { all_rows ; week 5 ; - } ; } ; february } } }, interpretation: select the rows whose week 5 record fuzzily matches to melisa jackson . take the record of this row . select the rows whose week 5 record fuzzily matches to - . take the record of this row . the first record is less than the second record . the record of the first row is january . the record of the second row is february .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; week 5 ; melisa jackson } ; } ; hop { filter_eq { all_rows ; week 5 ; - } ; } } ; and { eq { hop { filter_eq { all_rows ; week 5 ; melisa jackson } ; } ; january } ; eq { hop { filter_eq { all_rows ; week 5 ; - } ; } ; february } } }, interpretation: the average of the height in ft record of all rows is 6 - 9 .
Output: no
Command: round_eq { avg { all_rows ; height in ft } ; 6 - 9 }, interpretation: the average of the height in ft record of all rows is 6 - 9 .
Output: yes
Command: round_eq { avg { all_rows ; height in ft } ; 6 - 9 }, interpretation: for the gold records of all rows , most of them are greater than or equal to 1 .
Output: no
Command: most_greater_eq { all_rows ; gold ; 1 }, interpretation: for the gold records of all rows , most of them are greater than or equal to 1 .
Output: yes
Command: most_greater_eq { all_rows ; gold ; 1 }, interpretation: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 1 } } ; 2 }, interpretation: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 1 } } ; 2 }, interpretation: for the ihsaa class records of all rows , all of them fuzzily match to a .
Output: no
Command: all_eq { all_rows ; ihsaa class ; a }, interpretation: for the ihsaa class records of all rows , all of them fuzzily match to a .
Output: yes
Command: all_eq { all_rows ; ihsaa class ; a }, interpretation: select the rows whose date record is less than january 1 , 2000 . for the format records of these rows , most of them fuzzily match to cd .
Output: no
Command: most_eq { filter_less { all_rows ; date ; january 1 , 2000 } ; format ; cd }, interpretation: select the rows whose date record is less than january 1 , 2000 . for the format records of these rows , most of them fuzzily match to cd .
Output: yes
Command: most_eq { filter_less { all_rows ; date ; january 1 , 2000 } ; format ; cd }, interpretation: select the row whose power ( kw ) record of all rows is 2nd maximum . the branding record of this row is kingdom radio .
Output: no
Command: eq { hop { nth_argmax { all_rows ; power ( kw ) ; 2 } ; branding } ; kingdom radio }, interpretation: select the row whose power ( kw ) record of all rows is 2nd maximum . the branding record of this row is kingdom radio .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; power ( kw ) ; 2 } ; branding } ; kingdom radio }, interpretation: select the rows whose home team score record is less than 14.0 . among these rows , select the rows whose date record fuzzily matches to 28 april 1973 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_less { all_rows ; home team score ; 14.0 } ; date ; 28 april 1973 } } ; 2 }, interpretation: select the rows whose home team score record is less than 14.0 . among these rows , select the rows whose date record fuzzily matches to 28 april 1973 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_less { all_rows ; home team score ; 14.0 } ; date ; 28 april 1973 } } ; 2 }, interpretation: select the rows whose air date record fuzzily matches to july 2008 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; air date ; july 2008 } } ; 2 }, interpretation: select the rows whose air date record fuzzily matches to july 2008 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; air date ; july 2008 } } ; 2 }, interpretation: select the row whose attendance / g record of all rows is minimum . the season record of this row is 2007 .
Output: no
Command: eq { hop { argmin { all_rows ; attendance / g } ; season } ; 2007 }, interpretation: select the row whose attendance / g record of all rows is minimum . the season record of this row is 2007 .
Output: yes
Command: eq { hop { argmin { all_rows ; attendance / g } ; season } ; 2007 }, interpretation: the sum of the runs record of all rows is 2617 .
Output: no
Command: round_eq { sum { all_rows ; runs } ; 2617 }, interpretation: the sum of the runs record of all rows is 2617 .
Output: yes
Command: round_eq { sum { all_rows ; runs } ; 2617 }, interpretation: select the rows whose top speed ( km / h ) record is less than 175 . there is only one such row in the table . the model record of this unqiue row is 1.4 16v .
Output: no
Command: and { only { filter_less { all_rows ; top speed ( km / h ) ; 175 } } ; eq { hop { filter_less { all_rows ; top speed ( km / h ) ; 175 } ; model } ; 1.4 16v } }, interpretation: select the rows whose top speed ( km / h ) record is less than 175 . there is only one such row in the table . the model record of this unqiue row is 1.4 16v .
Output: yes
Command: and { only { filter_less { all_rows ; top speed ( km / h ) ; 175 } } ; eq { hop { filter_less { all_rows ; top speed ( km / h ) ; 175 } ; model } ; 1.4 16v } }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is mcg .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; mcg }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is mcg .
Output: yes
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; mcg }, interpretation: for the party records of all rows , all of them fuzzily match to democratic .
Output: no
Command: all_eq { all_rows ; party ; democratic }, interpretation: select the row whose time record of all rows is 1st minimum . the rank record of this row is 1 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 }, interpretation: select the row whose time record of all rows is 1st minimum . the rank record of this row is 1 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 }, interpretation: select the row whose enrollment record of all rows is 2nd minimum . the institution record of this row is philander smith college .
Output: no
Command: eq { hop { nth_argmin { all_rows ; enrollment ; 2 } ; institution } ; philander smith college }, interpretation: select the row whose enrollment record of all rows is 2nd minimum . the institution record of this row is philander smith college .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; enrollment ; 2 } ; institution } ; philander smith college }, interpretation: select the rows whose date record fuzzily matches to 16 october 2012 . take the score record of this row . select the rows whose date record fuzzily matches to 8 october 2010 . take the score record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; date ; 16 october 2012 } ; score } ; hop { filter_eq { all_rows ; date ; 8 october 2010 } ; score } }, interpretation: select the rows whose date record fuzzily matches to 16 october 2012 . take the score record of this row . select the rows whose date record fuzzily matches to 8 october 2010 . take the score record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; date ; 16 october 2012 } ; score } ; hop { filter_eq { all_rows ; date ; 8 october 2010 } ; score } }, interpretation: select the rows whose winner record fuzzily matches to 6 - 1 , 6 - 1 . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; winner ; 6 - 1 , 6 - 1 } }, interpretation: select the rows whose winner record fuzzily matches to 6 - 1 , 6 - 1 . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; winner ; 6 - 1 , 6 - 1 } }, interpretation: select the rows whose men 's singles record fuzzily matches to kasperi salo . take the year record of this row . select the rows whose men 's singles record fuzzily matches to ville lang . take the year record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; men 's singles ; kasperi salo } ; year } ; hop { filter_eq { all_rows ; men 's singles ; ville lang } ; year } }, interpretation: select the rows whose men 's singles record fuzzily matches to kasperi salo . take the year record of this row . select the rows whose men 's singles record fuzzily matches to ville lang . take the year record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; men 's singles ; kasperi salo } ; year } ; hop { filter_eq { all_rows ; men 's singles ; ville lang } ; year } }, interpretation: select the rows whose date record fuzzily matches to april 19 . take the high points record of this row . select the rows whose date record fuzzily matches to april 20 . take the high points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; april 19 } ; high points } ; hop { filter_eq { all_rows ; date ; april 20 } ; high points } }, interpretation: select the rows whose date record fuzzily matches to april 19 . take the high points record of this row . select the rows whose date record fuzzily matches to april 20 . take the high points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; april 19 } ; high points } ; hop { filter_eq { all_rows ; date ; april 20 } ; high points } }, interpretation: select the rows whose venue record fuzzily matches to amman . among these rows , select the rows whose result record fuzzily matches to win . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; venue ; amman } ; result ; win } } ; 6 }, interpretation: select the rows whose venue record fuzzily matches to amman . among these rows , select the rows whose result record fuzzily matches to win . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; venue ; amman } ; result ; win } } ; 6 }, interpretation: select the rows whose team record fuzzily matches to queens park rangers . take the date of vacancy record of this row . select the rows whose team record fuzzily matches to norwich city . take the date of vacancy record of this row . the second record is 7 days larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; team ; queens park rangers } ; date of vacancy } ; hop { filter_eq { all_rows ; team ; norwich city } ; date of vacancy } } ; -7 days }, interpretation: select the rows whose team record fuzzily matches to queens park rangers . take the date of vacancy record of this row . select the rows whose team record fuzzily matches to norwich city . take the date of vacancy record of this row . the second record is 7 days larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; team ; queens park rangers } ; date of vacancy } ; hop { filter_eq { all_rows ; team ; norwich city } ; date of vacancy } } ; -7 days }, interpretation: select the rows whose bronze record is equal to 34 . there is only one such row in the table . the rank record of this unqiue row is 5 .
Output: no
Command: and { only { filter_eq { all_rows ; bronze ; 34 } } ; eq { hop { filter_eq { all_rows ; bronze ; 34 } ; rank } ; 5 } }, interpretation: select the rows whose bronze record is equal to 34 . there is only one such row in the table . the rank record of this unqiue row is 5 .
Output: yes
Command: and { only { filter_eq { all_rows ; bronze ; 34 } } ; eq { hop { filter_eq { all_rows ; bronze ; 34 } ; rank } ; 5 } }, interpretation: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the track record of this unqiue row is little valley speedway .
Output: no
Command: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway } }, interpretation: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the track record of this unqiue row is little valley speedway .
Output: yes
Command: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway } }, interpretation: select the rows whose hometown record fuzzily matches to santiago de los caballeros . among these rows , select the rows whose age record is less than 19 . there is only one such row in the table . the contestant record of this unqiue row is valerie chardonnens vargas .
Output: no
Command: and { only { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } } ; eq { hop { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } ; contestant } ; valerie chardonnens vargas } }, interpretation: select the rows whose hometown record fuzzily matches to santiago de los caballeros . among these rows , select the rows whose age record is less than 19 . there is only one such row in the table . the contestant record of this unqiue row is valerie chardonnens vargas .
Output: yes
Command: and { only { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } } ; eq { hop { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } ; contestant } ; valerie chardonnens vargas } }, interpretation: select the rows whose member countries record fuzzily matches to malta . take the gdp per capita ( us ) record of this row . select the rows whose member countries record fuzzily matches to czech republic . take the gdp per capita ( us ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; member countries ; malta } ; gdp per capita ( us ) } ; hop { filter_eq { all_rows ; member countries ; czech republic } ; gdp per capita ( us ) } }, interpretation: select the rows whose member countries record fuzzily matches to malta . take the gdp per capita ( us ) record of this row . select the rows whose member countries record fuzzily matches to czech republic . take the gdp per capita ( us ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; member countries ; malta } ; gdp per capita ( us ) } ; hop { filter_eq { all_rows ; member countries ; czech republic } ; gdp per capita ( us ) } }, interpretation: select the rows whose player record fuzzily matches to yordanos abay . take the goals record of this row . select the rows whose player record fuzzily matches to fathi jabir . take the goals record of this row . the second record is 15 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; player ; yordanos abay } ; goals } ; hop { filter_eq { all_rows ; player ; fathi jabir } ; goals } } ; -15 }, interpretation: select the rows whose player record fuzzily matches to yordanos abay . take the goals record of this row . select the rows whose player record fuzzily matches to fathi jabir . take the goals record of this row . the second record is 15 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; player ; yordanos abay } ; goals } ; hop { filter_eq { all_rows ; player ; fathi jabir } ; goals } } ; -15 }, interpretation: select the rows whose year record fuzzily matches to 1975 . take the stages record of this row . select the rows whose year record fuzzily matches to 1976 . take the stages record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 1975 } ; stages } ; hop { filter_eq { all_rows ; year ; 1976 } ; stages } }, interpretation: select the rows whose year record fuzzily matches to 1975 . take the stages record of this row . select the rows whose year record fuzzily matches to 1976 . take the stages record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 1975 } ; stages } ; hop { filter_eq { all_rows ; year ; 1976 } ; stages } }, interpretation: for the driver records of all rows , all of them fuzzily match to alessandro pier guidi .
Output: no
Command: all_eq { all_rows ; driver ; alessandro pier guidi }, interpretation: for the driver records of all rows , all of them fuzzily match to alessandro pier guidi .
Output: yes
Command: all_eq { all_rows ; driver ; alessandro pier guidi }, interpretation: for the country records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; country ; united states }, interpretation: select the rows whose incumbent record fuzzily matches to otto passman . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to t ashton thompson . 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 ; otto passman } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; t ashton thompson } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to otto passman . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to t ashton thompson . 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 ; otto passman } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; t ashton thompson } ; first elected } }, interpretation: the sum of the score record of all rows is 29 .
Output: no
Command: round_eq { sum { all_rows ; score } ; 29 }, interpretation: the sum of the score record of all rows is 29 .
Output: yes
Command: round_eq { sum { all_rows ; score } ; 29 }, interpretation: select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row . select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row . the second record is 1 larger than the first record . the episode record of the first row is 151 . the episode record of the second row is 152 .
Output: no
Command: and { eq { diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 } ; eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 } } }, interpretation: select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row . select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row . the second record is 1 larger than the first record . the episode record of the first row is 151 . the episode record of the second row is 152 .
Output: yes
Command: and { eq { diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 } ; eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 } } }, interpretation: select the rows whose date record fuzzily matches to friday , 16 february . the sum of the crowd record of these rows is 46,000 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; friday , 16 february } ; crowd } ; 46,000 }, interpretation: select the rows whose date record fuzzily matches to friday , 16 february . the sum of the crowd record of these rows is 46,000 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; friday , 16 february } ; crowd } ; 46,000 }, interpretation: select the row whose number record of all rows is maximum . the unit numbers record of this row is 254001 - 254032 .
Output: no
Command: eq { hop { argmax { all_rows ; number } ; unit numbers } ; 254001 - 254032 }, interpretation: select the row whose number record of all rows is maximum . the unit numbers record of this row is 254001 - 254032 .
Output: yes
Command: eq { hop { argmax { all_rows ; number } ; unit numbers } ; 254001 - 254032 }, interpretation: select the row whose quota record of all rows is 2nd maximum . the constituency record of this row is connachtulster .
Output: no
Command: eq { hop { nth_argmax { all_rows ; quota ; 2 } ; constituency } ; connachtulster }, interpretation: select the row whose quota record of all rows is 2nd maximum . the constituency record of this row is connachtulster .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; quota ; 2 } ; constituency } ; connachtulster }, interpretation: select the row whose foundation record of all rows is 2nd minimum . the english name record of this row is japan coast guard academy .
Output: no
Command: eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; english name } ; japan coast guard academy }, interpretation: select the row whose foundation record of all rows is 2nd minimum . the english name record of this row is japan coast guard academy .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; english name } ; japan coast guard academy }, interpretation: for the viewers ( million ) records of all rows , most of them are greater than 3 .
Output: no
Command: most_greater { all_rows ; viewers ( million ) ; 3 }, interpretation: for the viewers ( million ) records of all rows , most of them are greater than 3 .
Output: yes
Command: most_greater { all_rows ; viewers ( million ) ; 3 }, interpretation: select the rows whose category record fuzzily matches to outstanding actor . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; category ; outstanding actor } } ; 3 }, interpretation: select the rows whose category record fuzzily matches to outstanding actor . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; category ; outstanding actor } } ; 3 }, interpretation: select the row whose location attendance record of all rows is 2nd maximum . the team record of this row is dallas .
Output: no
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; team } ; dallas }, interpretation: select the row whose location attendance record of all rows is 2nd maximum . the team record of this row is dallas .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; team } ; dallas }, interpretation: select the rows whose result record fuzzily matches to t . there is only one such row in the table . the date record of this unqiue row is october 2 , 1953 . the opponent record of this unqiue row is philadelphia eagles .
Output: no
Command: and { only { filter_eq { all_rows ; result ; t } } ; and { eq { hop { filter_eq { all_rows ; result ; t } ; date } ; october 2 , 1953 } ; eq { hop { filter_eq { all_rows ; result ; t } ; opponent } ; philadelphia eagles } } }, interpretation: select the rows whose result record fuzzily matches to t . there is only one such row in the table . the date record of this unqiue row is october 2 , 1953 . the opponent record of this unqiue row is philadelphia eagles .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; t } } ; and { eq { hop { filter_eq { all_rows ; result ; t } ; date } ; october 2 , 1953 } ; eq { hop { filter_eq { all_rows ; result ; t } ; opponent } ; philadelphia eagles } } }, interpretation: select the rows whose written by record fuzzily matches to lucy watkins . there is only one such row in the table . the title record of this unqiue row is sweet dreams .
Output: no
Command: and { only { filter_eq { all_rows ; written by ; lucy watkins } } ; eq { hop { filter_eq { all_rows ; written by ; lucy watkins } ; title } ; sweet dreams } }, interpretation: select the rows whose written by record fuzzily matches to lucy watkins . there is only one such row in the table . the title record of this unqiue row is sweet dreams .
Output: yes
Command: and { only { filter_eq { all_rows ; written by ; lucy watkins } } ; eq { hop { filter_eq { all_rows ; written by ; lucy watkins } ; title } ; sweet dreams } }, interpretation: select the row whose design flow ( lpm ) record of all rows is maximum . the location record of this row is marcala , hon .
Output: no
Command: eq { hop { argmax { all_rows ; design flow ( lpm ) } ; location } ; marcala , hon }, interpretation: select the row whose design flow ( lpm ) record of all rows is maximum . the location record of this row is marcala , hon .
Output: yes
Command: eq { hop { argmax { all_rows ; design flow ( lpm ) } ; location } ; marcala , hon }, interpretation: select the rows whose size ( steps ) record is greater than 4 . the average of the size ( cents ) record of these rows is 510 .
Output: no
Command: round_eq { avg { filter_greater { all_rows ; size ( steps ) ; 4 } ; size ( cents ) } ; 510 }, interpretation: select the rows whose size ( steps ) record is greater than 4 . the average of the size ( cents ) record of these rows is 510 .
Output: yes
Command: round_eq { avg { filter_greater { all_rows ; size ( steps ) ; 4 } ; size ( cents ) } ; 510 }, interpretation: select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .
Output: no
Command: and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } }, interpretation: select the rows whose name record fuzzily matches to richard schryer . take the round record of this row . select the rows whose name record fuzzily matches to james carter . take the round record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; richard schryer } ; round } ; hop { filter_eq { all_rows ; name ; james carter } ; round } }, interpretation: select the rows whose name record fuzzily matches to richard schryer . take the round record of this row . select the rows whose name record fuzzily matches to james carter . take the round record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; richard schryer } ; round } ; hop { filter_eq { all_rows ; name ; james carter } ; round } }, interpretation: select the rows whose years in orlando record fuzzily matches to present . there is only one such row in the table . the player record of this unqiue row is victor oladipo .
Output: no
Command: and { only { filter_eq { all_rows ; years in orlando ; present } } ; eq { hop { filter_eq { all_rows ; years in orlando ; present } ; player } ; victor oladipo } }, interpretation: select the rows whose years in orlando record fuzzily matches to present . there is only one such row in the table . the player record of this unqiue row is victor oladipo .
Output: yes
Command: and { only { filter_eq { all_rows ; years in orlando ; present } } ; eq { hop { filter_eq { all_rows ; years in orlando ; present } ; player } ; victor oladipo } }, interpretation: select the row whose year joined record of all rows is 1st maximum . the school record of this row is elkhart christian .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year joined ; 1 } ; school } ; elkhart christian }, interpretation: select the row whose year joined record of all rows is 1st maximum . the school record of this row is elkhart christian .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year joined ; 1 } ; school } ; elkhart christian }, interpretation: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } ; 1 }, interpretation: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } ; 1 }, interpretation: select the rows whose team record fuzzily matches to bms scuderia italia . take the laps record of this row . select the rows whose team record fuzzily matches to jb racing . take the laps record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; team ; bms scuderia italia } ; laps } ; hop { filter_eq { all_rows ; team ; jb racing } ; laps } }, interpretation: select the rows whose team record fuzzily matches to bms scuderia italia . take the laps record of this row . select the rows whose team record fuzzily matches to jb racing . take the laps record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; team ; bms scuderia italia } ; laps } ; hop { filter_eq { all_rows ; team ; jb racing } ; laps } }, interpretation: select the row whose took office record of all rows is 4th minimum . the delegate record of this row is brian k mchale .
Output: no
Command: eq { hop { nth_argmin { all_rows ; took office ; 4 } ; delegate } ; brian k mchale }, interpretation: select the row whose took office record of all rows is 4th minimum . the delegate record of this row is brian k mchale .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; took office ; 4 } ; delegate } ; brian k mchale }, interpretation: for the nationality records of all rows , all of them fuzzily match to canada .
Output: no
Command: all_eq { all_rows ; nationality ; canada }, interpretation: for the nationality records of all rows , all of them fuzzily match to canada .
Output: yes
Command: all_eq { all_rows ; nationality ; canada }, interpretation: select the rows whose model record fuzzily matches to 325ti . take the power record of this row . select the rows whose model record fuzzily matches to 316ti . take the power record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; model ; 325ti } ; power } ; hop { filter_eq { all_rows ; model ; 316ti } ; power } }, interpretation: select the rows whose model record fuzzily matches to 325ti . take the power record of this row . select the rows whose model record fuzzily matches to 316ti . take the power record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; model ; 325ti } ; power } ; hop { filter_eq { all_rows ; model ; 316ti } ; power } }, interpretation: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 4 }, interpretation: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 4 }, interpretation: select the rows whose visitor record fuzzily matches to chicago black hawks . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; visitor ; chicago black hawks } } ; 3 }, interpretation: select the rows whose visitor record fuzzily matches to chicago black hawks . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; visitor ; chicago black hawks } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to december . for the attendance records of these rows , most of them are greater than 60000 .
Output: no
Command: most_greater { filter_eq { all_rows ; date ; december } ; attendance ; 60000 }, interpretation: select the rows whose date record fuzzily matches to december . for the attendance records of these rows , most of them are greater than 60000 .
Output: yes
Command: most_greater { filter_eq { all_rows ; date ; december } ; attendance ; 60000 }, interpretation: select the rows whose year record is equal to 2008 . among these rows , select the rows whose length record is greater than 5:00 . there is only one such row in the table . the music video record of this unqiue row is umbrella ( feat younha ) ( 우산 ) .
Output: no
Command: and { only { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } } ; eq { hop { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } ; music video } ; umbrella ( feat younha ) ( 우산 ) } }, interpretation: select the rows whose year record is equal to 2008 . among these rows , select the rows whose length record is greater than 5:00 . there is only one such row in the table . the music video record of this unqiue row is umbrella ( feat younha ) ( 우산 ) .
Output: yes
Command: and { only { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } } ; eq { hop { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } ; music video } ; umbrella ( feat younha ) ( 우산 ) } }, interpretation: select the rows whose round record is equal to 2 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; round ; 2 } } ; 3 }, interpretation: select the rows whose round record is equal to 2 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; round ; 2 } } ; 3 }, interpretation: for the coach records of all rows , all of them fuzzily match to john cartwright .
Output: no
Command: all_eq { all_rows ; coach ; john cartwright }, interpretation: for the coach records of all rows , all of them fuzzily match to john cartwright .
Output: yes
Command: all_eq { all_rows ; coach ; john cartwright }, interpretation: select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to essendon . take the home team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; essendon } ; home team score } }, interpretation: select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to essendon . take the home team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; essendon } ; home team score } }, interpretation: for the date records of all rows , all of them fuzzily match to 2 august 1980 .
Output: no
Command: all_eq { all_rows ; date ; 2 august 1980 }, interpretation: for the date records of all rows , all of them fuzzily match to 2 august 1980 .
Output: yes
Command: all_eq { all_rows ; date ; 2 august 1980 }, interpretation: select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john little mcclellan . 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 ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john little mcclellan } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john little mcclellan . 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 ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john little mcclellan } ; first elected } }, interpretation: select the rows whose player record fuzzily matches to ed furgol . take the money record of this row . select the rows whose player record fuzzily matches to lloyd mangrum . take the money record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; ed furgol } ; money } ; hop { filter_eq { all_rows ; player ; lloyd mangrum } ; money } }, interpretation: select the rows whose player record fuzzily matches to ed furgol . take the money record of this row . select the rows whose player record fuzzily matches to lloyd mangrum . take the money record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; ed furgol } ; money } ; hop { filter_eq { all_rows ; player ; lloyd mangrum } ; money } }, interpretation: select the rows whose women 's singles record fuzzily matches to sara persson . there is only one such row in the table . the year record of this unqiue row is 2006 .
Output: no
Command: and { only { filter_eq { all_rows ; women 's singles ; sara persson } } ; eq { hop { filter_eq { all_rows ; women 's singles ; sara persson } ; year } ; 2006 } }, interpretation: select the rows whose women 's singles record fuzzily matches to sara persson . there is only one such row in the table . the year record of this unqiue row is 2006 .
Output: yes
Command: and { only { filter_eq { all_rows ; women 's singles ; sara persson } } ; eq { hop { filter_eq { all_rows ; women 's singles ; sara persson } ; year } ; 2006 } }, interpretation: select the rows whose team record fuzzily matches to aermacchi . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; team ; aermacchi } }, interpretation: select the rows whose team record fuzzily matches to aermacchi . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; team ; aermacchi } }, interpretation: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the tournament record of this unqiue row is valencia , spain .
Output: no
Command: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; tournament } ; valencia , spain } }, interpretation: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the tournament record of this unqiue row is valencia , spain .
Output: yes
Command: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; tournament } ; valencia , spain } }, interpretation: select the rows whose place record fuzzily matches to pietersburg . take the population record of this row . select the rows whose place record fuzzily matches to alldays . take the population record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; place ; pietersburg } ; population } ; hop { filter_eq { all_rows ; place ; alldays } ; population } }, interpretation: select the rows whose place record fuzzily matches to pietersburg . take the population record of this row . select the rows whose place record fuzzily matches to alldays . take the population record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; place ; pietersburg } ; population } ; hop { filter_eq { all_rows ; place ; alldays } ; population } }, interpretation: select the rows whose candidate 's name record fuzzily matches to michael mackay . take the rank record of this row . select the rows whose candidate 's name record fuzzily matches to joe larkin . take the rank record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; candidate 's name ; michael mackay } ; rank } ; hop { filter_eq { all_rows ; candidate 's name ; joe larkin } ; rank } }, interpretation: select the rows whose candidate 's name record fuzzily matches to michael mackay . take the rank record of this row . select the rows whose candidate 's name record fuzzily matches to joe larkin . take the rank record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; candidate 's name ; michael mackay } ; rank } ; hop { filter_eq { all_rows ; candidate 's name ; joe larkin } ; rank } }, interpretation: select the rows whose pick record is equal to 19 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; pick ; 19 } } ; 3 }, interpretation: select the rows whose pick record is equal to 19 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; pick ; 19 } } ; 3 }, interpretation: for the issue price records of all rows , most of them are less than 100.0 .
Output: no
Command: most_less { all_rows ; issue price ; 100.0 }, interpretation: for the issue price records of all rows , most of them are less than 100.0 .
Output: yes
Command: most_less { all_rows ; issue price ; 100.0 }, interpretation: select the rows whose date introduced record fuzzily matches to june . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; date introduced ; june } } ; 5 }, interpretation: select the rows whose date introduced record fuzzily matches to june . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date introduced ; june } } ; 5 }, interpretation: the average of the leading scorer record of all rows is 28.5 .
Output: no
Command: round_eq { avg { all_rows ; leading scorer } ; 28.5 }, interpretation: the average of the leading scorer record of all rows is 28.5 .
Output: yes
Command: round_eq { avg { all_rows ; leading scorer } ; 28.5 }, interpretation: for the wins records of all rows , most of them are less than 20 .
Output: no