niryuu/sni-each-converted huggingface dataset:
Command: and { only { filter_eq { all_rows ; position ; tight end } } ; eq { hop { filter_eq { all_rows ; position ; tight end } ; player } ; spencer nead } }, interpretation: select the rows whose event record fuzzily matches to ept baden classic . take the prize record of this row . select the rows whose event record fuzzily matches to the european poker championships . take the prize record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; event ; ept baden classic } ; prize } ; hop { filter_eq { all_rows ; event ; the european poker championships } ; prize } }, interpretation: select the rows whose event record fuzzily matches to ept baden classic . take the prize record of this row . select the rows whose event record fuzzily matches to the european poker championships . take the prize record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; event ; ept baden classic } ; prize } ; hop { filter_eq { all_rows ; event ; the european poker championships } ; prize } }, interpretation: the sum of the bb + hbp record of all rows is 580 .
Output: no
Command: round_eq { sum { all_rows ; bb + hbp } ; 580 }, interpretation: the sum of the bb + hbp record of all rows is 580 .
Output: yes
Command: round_eq { sum { all_rows ; bb + hbp } ; 580 }, interpretation: the average of the time record of all rows is 13.4 .
Output: no
Command: round_eq { avg { all_rows ; time } ; 13.4 }, interpretation: the average of the time record of all rows is 13.4 .
Output: yes
Command: round_eq { avg { all_rows ; time } ; 13.4 }, interpretation: select the rows whose reason for change record fuzzily matches to died . there is only one such row in the table . the vacator record of this unqiue row is thomas blount ( dr ) .
Output: no
Command: and { only { filter_eq { all_rows ; reason for change ; died } } ; eq { hop { filter_eq { all_rows ; reason for change ; died } ; vacator } ; thomas blount ( dr ) } }, interpretation: select the rows whose reason for change record fuzzily matches to died . there is only one such row in the table . the vacator record of this unqiue row is thomas blount ( dr ) .
Output: yes
Command: and { only { filter_eq { all_rows ; reason for change ; died } } ; eq { hop { filter_eq { all_rows ; reason for change ; died } ; vacator } ; thomas blount ( dr ) } }, interpretation: select the rows whose player record fuzzily matches to juan theron . take the overs record of this row . select the rows whose player record fuzzily matches to makhaya ntini . take the overs record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; juan theron } ; overs } ; hop { filter_eq { all_rows ; player ; makhaya ntini } ; overs } }, interpretation: select the rows whose player record fuzzily matches to juan theron . take the overs record of this row . select the rows whose player record fuzzily matches to makhaya ntini . take the overs record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; juan theron } ; overs } ; hop { filter_eq { all_rows ; player ; makhaya ntini } ; overs } }, interpretation: select the rows whose manufacturer record fuzzily matches to suzuki . the average of the laps record of these rows is 21.5 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; manufacturer ; suzuki } ; laps } ; 21.5 }, interpretation: select the rows whose manufacturer record fuzzily matches to suzuki . the average of the laps record of these rows is 21.5 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; manufacturer ; suzuki } ; laps } ; 21.5 }, interpretation: for the party records of all rows , most of them fuzzily match to democrat .
Output: no
Command: most_eq { all_rows ; party ; democrat }, interpretation: for the party records of all rows , most of them fuzzily match to democrat .
Output: yes
Command: most_eq { all_rows ; party ; democrat }, interpretation: select the rows whose country record fuzzily matches to sweden . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; sweden } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to sweden . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; sweden } } ; 2 }, interpretation: select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record . the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } } ; and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } } }, interpretation: select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record . the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } } ; and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } } }, interpretation: select the row whose date of vacancy record of all rows is 2nd maximum . the team record of this row is coventry city .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date of vacancy ; 2 } ; team } ; coventry city }, interpretation: select the row whose date of vacancy record of all rows is 2nd maximum . the team record of this row is coventry city .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date of vacancy ; 2 } ; team } ; coventry city }, interpretation: select the rows whose rider record fuzzily matches to wattie brown . take the time record of this row . select the rows whose rider record fuzzily matches to chris swallow . take the time record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; rider ; wattie brown } ; time } ; hop { filter_eq { all_rows ; rider ; chris swallow } ; time } }, interpretation: select the rows whose rider record fuzzily matches to wattie brown . take the time record of this row . select the rows whose rider record fuzzily matches to chris swallow . take the time record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; rider ; wattie brown } ; time } ; hop { filter_eq { all_rows ; rider ; chris swallow } ; time } }, interpretation: select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 2 }, interpretation: select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 2 }, interpretation: the average of the crowd record of all rows is 16667 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 16667 }, interpretation: the average of the crowd record of all rows is 16667 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 16667 }, interpretation: select the row whose overall record of all rows is 2nd minimum . the name record of this row is charley holm .
Output: no
Command: eq { hop { nth_argmin { all_rows ; overall ; 2 } ; name } ; charley holm }, interpretation: select the row whose overall record of all rows is 2nd minimum . the name record of this row is charley holm .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; overall ; 2 } ; name } ; charley holm }, interpretation: select the rows whose partner record fuzzily matches to tatiana poutchek . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; partner ; tatiana poutchek } } ; 2 }, interpretation: select the rows whose partner record fuzzily matches to tatiana poutchek . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; partner ; tatiana poutchek } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; united states } } ; 5 }, interpretation: select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; united states } } ; 5 }, interpretation: for the station type records of all rows , most of them fuzzily match to relay .
Output: no
Command: most_eq { all_rows ; station type ; relay }, interpretation: select the row whose no in series record of all rows is 1st maximum . the family / families record of this row is the williams family .
Output: no
Command: eq { hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families } ; the williams family }, interpretation: select the row whose no in series record of all rows is 1st maximum . the family / families record of this row is the williams family .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families } ; the williams family }, interpretation: select the rows whose qual 1 record is equal to - . there is only one such row in the table . the name record of this unqiue row is a j allmendinger .
Output: no
Command: and { only { filter_eq { all_rows ; qual 1 ; - } } ; eq { hop { filter_eq { all_rows ; qual 1 ; - } ; name } ; a j allmendinger } }, interpretation: select the rows whose qual 1 record is equal to - . there is only one such row in the table . the name record of this unqiue row is a j allmendinger .
Output: yes
Command: and { only { filter_eq { all_rows ; qual 1 ; - } } ; eq { hop { filter_eq { all_rows ; qual 1 ; - } ; name } ; a j allmendinger } }, interpretation: select the rows whose erp w record is equal to 33 . there is only one such row in the table . the call sign record of this unqiue row is w267 am .
Output: no
Command: and { only { filter_eq { all_rows ; erp w ; 33 } } ; eq { hop { filter_eq { all_rows ; erp w ; 33 } ; call sign } ; w267 am } }, interpretation: select the rows whose erp w record is equal to 33 . there is only one such row in the table . the call sign record of this unqiue row is w267 am .
Output: yes
Command: and { only { filter_eq { all_rows ; erp w ; 33 } } ; eq { hop { filter_eq { all_rows ; erp w ; 33 } ; call sign } ; w267 am } }, interpretation: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is cleveland .
Output: no
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; cleveland }, interpretation: select the row whose location attendance record of all rows is 1st maximum . the team record of this row is cleveland .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; cleveland }, interpretation: select the rows whose opponent in the final record fuzzily matches to sania mirza . there is only one such row in the table . the championship record of this unqiue row is hyderabad , india .
Output: no
Command: and { only { filter_eq { all_rows ; opponent in the final ; sania mirza } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; sania mirza } ; championship } ; hyderabad , india } }, interpretation: select the rows whose opponent in the final record fuzzily matches to sania mirza . there is only one such row in the table . the championship record of this unqiue row is hyderabad , india .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent in the final ; sania mirza } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; sania mirza } ; championship } ; hyderabad , india } }, interpretation: the average of the total record of all rows is 2.13 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 2.13 }, interpretation: the average of the total record of all rows is 2.13 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 2.13 }, interpretation: select the rows whose rank record is less than or equal to 5 . the average of the bronze record of these rows is 4 .
Output: no
Command: round_eq { avg { filter_less_eq { all_rows ; rank ; 5 } ; bronze } ; 4 }, interpretation: select the rows whose rank record is less than or equal to 5 . the average of the bronze record of these rows is 4 .
Output: yes
Command: round_eq { avg { filter_less_eq { all_rows ; rank ; 5 } ; bronze } ; 4 }, interpretation: for the result records of all rows , most of them fuzzily match to won .
Output: no
Command: most_eq { all_rows ; result ; won }, interpretation: select the row whose time record of all rows is 3rd minimum . the athlete record of this row is gabriella bascelli .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 3 } ; athlete } ; gabriella bascelli }, interpretation: select the row whose time record of all rows is 3rd minimum . the athlete record of this row is gabriella bascelli .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 3 } ; athlete } ; gabriella bascelli }, interpretation: select the rows whose college record fuzzily matches to mercer . select the row whose pick record of these rows is 1st minimum . the player record of this row is sam mitchell .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; college ; mercer } ; pick ; 1 } ; player } ; sam mitchell }, interpretation: select the rows whose college record fuzzily matches to mercer . select the row whose pick record of these rows is 1st minimum . the player record of this row is sam mitchell .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; college ; mercer } ; pick ; 1 } ; player } ; sam mitchell }, interpretation: the sum of the against record of all rows is 121 .
Output: no
Command: round_eq { sum { all_rows ; against } ; 121 }, interpretation: the sum of the against record of all rows is 121 .
Output: yes
Command: round_eq { sum { all_rows ; against } ; 121 }, interpretation: select the row whose points record of all rows is maximum . the driver record of this row is will power .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; driver } ; will power }, interpretation: select the row whose points record of all rows is maximum . the driver record of this row is will power .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; driver } ; will power }, interpretation: select the rows whose score record fuzzily matches to l . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { all_rows ; score ; l } } ; 1 }, interpretation: select the rows whose score record fuzzily matches to l . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { all_rows ; score ; l } } ; 1 }, interpretation: select the rows whose total record is greater than 1 . among these rows , select the rows whose bronze record is equal to 0 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; total ; 1 } ; bronze ; 0 } } ; 2 }, interpretation: select the rows whose total record is greater than 1 . among these rows , select the rows whose bronze record is equal to 0 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; total ; 1 } ; bronze ; 0 } } ; 2 }, interpretation: the average of the wickets record of all rows is 33 .
Output: no
Command: round_eq { avg { all_rows ; wickets } ; 33 }, interpretation: the average of the wickets record of all rows is 33 .
Output: yes
Command: round_eq { avg { all_rows ; wickets } ; 33 }, interpretation: select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 3 }, interpretation: select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 3 }, interpretation: the average of the us viewers ( millions ) record of all rows is 17.89 .
Output: no
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 17.89 }, interpretation: the average of the us viewers ( millions ) record of all rows is 17.89 .
Output: yes
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 17.89 }, interpretation: the maximum goals record of all rows is 1468 . the country record of the row with superlative goals record is austria czech republic .
Output: no
Command: and { eq { max { all_rows ; goals } ; 1468 } ; eq { hop { argmax { all_rows ; goals } ; country } ; austria czech republic } }, interpretation: the maximum goals record of all rows is 1468 . the country record of the row with superlative goals record is austria czech republic .
Output: yes
Command: and { eq { max { all_rows ; goals } ; 1468 } ; eq { hop { argmax { all_rows ; goals } ; country } ; austria czech republic } }, interpretation: select the rows whose position record is greater than 10 . there is only one such row in the table . the year record of this unqiue row is 2008 .
Output: no
Command: and { only { filter_greater { all_rows ; position ; 10 } } ; eq { hop { filter_greater { all_rows ; position ; 10 } ; year } ; 2008 } }, interpretation: select the rows whose position record is greater than 10 . there is only one such row in the table . the year record of this unqiue row is 2008 .
Output: yes
Command: and { only { filter_greater { all_rows ; position ; 10 } } ; eq { hop { filter_greater { all_rows ; position ; 10 } ; year } ; 2008 } }, interpretation: select the rows whose shirt sponsor record fuzzily matches to n / a . there is only one such row in the table . the team record of this unqiue row is dubai .
Output: no
Command: and { only { filter_eq { all_rows ; shirt sponsor ; n / a } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; n / a } ; team } ; dubai } }, interpretation: select the rows whose shirt sponsor record fuzzily matches to n / a . there is only one such row in the table . the team record of this unqiue row is dubai .
Output: yes
Command: and { only { filter_eq { all_rows ; shirt sponsor ; n / a } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; n / a } ; team } ; dubai } }, interpretation: select the rows whose original air date record fuzzily matches to august . select the row whose us viewers ( million ) record of these rows is 2nd maximum . the title record of this row is a little death .
Output: no
Command: eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; august } ; us viewers ( million ) ; 2 } ; title } ; a little death }, interpretation: select the rows whose original air date record fuzzily matches to august . select the row whose us viewers ( million ) record of these rows is 2nd maximum . the title record of this row is a little death .
Output: yes
Command: eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; august } ; us viewers ( million ) ; 2 } ; title } ; a little death }, interpretation: select the rows whose school / club team record fuzzily matches to penn state . there is only one such row in the table . the player record of this unqiue row is john amaechi .
Output: no
Command: and { only { filter_eq { all_rows ; school / club team ; penn state } } ; eq { hop { filter_eq { all_rows ; school / club team ; penn state } ; player } ; john amaechi } }, interpretation: select the rows whose school / club team record fuzzily matches to penn state . there is only one such row in the table . the player record of this unqiue row is john amaechi .
Output: yes
Command: and { only { filter_eq { all_rows ; school / club team ; penn state } } ; eq { hop { filter_eq { all_rows ; school / club team ; penn state } ; player } ; john amaechi } }, interpretation: for the 5th venue records of all rows , all of them fuzzily match to na .
Output: no
Command: all_eq { all_rows ; 5th venue ; na }, interpretation: for the 5th venue records of all rows , all of them fuzzily match to na .
Output: yes
Command: all_eq { all_rows ; 5th venue ; na }, interpretation: select the row whose position record of all rows is minimum . the season record of this row is 2006 .
Output: no
Command: eq { hop { argmin { all_rows ; position } ; season } ; 2006 }, interpretation: select the row whose position record of all rows is minimum . the season record of this row is 2006 .
Output: yes
Command: eq { hop { argmin { all_rows ; position } ; season } ; 2006 }, interpretation: select the row whose date record of all rows is 11th minimum . the field record of this row is cawley memorial stadium .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 11 } ; field } ; cawley memorial stadium }, interpretation: select the row whose date record of all rows is 11th minimum . the field record of this row is cawley memorial stadium .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 11 } ; field } ; cawley memorial stadium }, interpretation: select the rows whose home record fuzzily matches to ny rangers . there is only one such row in the table . the date record of this unqiue row is december 1 .
Output: no
Command: and { only { filter_eq { all_rows ; home ; ny rangers } } ; eq { hop { filter_eq { all_rows ; home ; ny rangers } ; date } ; december 1 } }, interpretation: select the rows whose home record fuzzily matches to ny rangers . there is only one such row in the table . the date record of this unqiue row is december 1 .
Output: yes
Command: and { only { filter_eq { all_rows ; home ; ny rangers } } ; eq { hop { filter_eq { all_rows ; home ; ny rangers } ; date } ; december 1 } }, interpretation: select the rows whose municipality record fuzzily matches to trieste . take the inhabitants record of this row . select the rows whose municipality record fuzzily matches to udine . take the inhabitants record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; municipality ; trieste } ; inhabitants } ; hop { filter_eq { all_rows ; municipality ; udine } ; inhabitants } }, interpretation: select the rows whose municipality record fuzzily matches to trieste . take the inhabitants record of this row . select the rows whose municipality record fuzzily matches to udine . take the inhabitants record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; municipality ; trieste } ; inhabitants } ; hop { filter_eq { all_rows ; municipality ; udine } ; inhabitants } }, interpretation: select the row whose attendance record of all rows is maximum . the week record of this row is 11 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; week } ; 11 }, interpretation: select the row whose attendance record of all rows is maximum . the week record of this row is 11 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; week } ; 11 }, interpretation: select the row whose population record of all rows is maximum . the english name record of this row is hedong district .
Output: no
Command: eq { hop { argmax { all_rows ; population } ; english name } ; hedong district }, interpretation: select the row whose population record of all rows is maximum . the english name record of this row is hedong district .
Output: yes
Command: eq { hop { argmax { all_rows ; population } ; english name } ; hedong district }, interpretation: the 4th minimum date record of all rows is 21 july 2013 . the opponent in the final record of the row with 4th minimum date record is federico delbonis .
Output: no
Command: and { eq { nth_min { all_rows ; date ; 4 } ; 21 july 2013 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; opponent in the final } ; federico delbonis } }, interpretation: the 4th minimum date record of all rows is 21 july 2013 . the opponent in the final record of the row with 4th minimum date record is federico delbonis .
Output: yes
Command: and { eq { nth_min { all_rows ; date ; 4 } ; 21 july 2013 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; opponent in the final } ; federico delbonis } }, interpretation: select the row whose wins record of all rows is maximum . the player record of this row is tom kite .
Output: no
Command: eq { hop { argmax { all_rows ; wins } ; player } ; tom kite }, interpretation: select the row whose wins record of all rows is maximum . the player record of this row is tom kite .
Output: yes
Command: eq { hop { argmax { all_rows ; wins } ; player } ; tom kite }, interpretation: for the date records of all rows , all of them fuzzily match to april .
Output: no
Command: all_eq { all_rows ; date ; april }, interpretation: for the date records of all rows , all of them fuzzily match to april .
Output: yes
Command: all_eq { all_rows ; date ; april }, interpretation: select the row whose erp / power w record of all rows is 2nd maximum . the call sign record of this row is wrli - fm .
Output: no
Command: eq { hop { nth_argmax { all_rows ; erp / power w ; 2 } ; call sign } ; wrli - fm }, interpretation: select the row whose erp / power w record of all rows is 2nd maximum . the call sign record of this row is wrli - fm .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; erp / power w ; 2 } ; call sign } ; wrli - fm }, interpretation: select the rows whose opposing teams record fuzzily matches to france . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opposing teams ; france } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } }, interpretation: select the rows whose opposing teams record fuzzily matches to france . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opposing teams ; france } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } }, interpretation: select the rows whose first elected record fuzzily matches to 1878 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; first elected ; 1878 } } ; 3 }, interpretation: select the rows whose first elected record fuzzily matches to 1878 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first elected ; 1878 } } ; 3 }, interpretation: select the row whose year record of all rows is 2nd minimum . the author record of this row is anthony browne .
Output: no
Command: eq { hop { nth_argmin { all_rows ; year ; 2 } ; author } ; anthony browne }, interpretation: select the row whose year record of all rows is 2nd minimum . the author record of this row is anthony browne .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; year ; 2 } ; author } ; anthony browne }, interpretation: select the rows whose driver record fuzzily matches to sarah fisher . take the laps record of this row . select the rows whose driver record fuzzily matches to dan wheldon . 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 ; driver ; sarah fisher } ; laps } ; hop { filter_eq { all_rows ; driver ; dan wheldon } ; laps } }, interpretation: select the rows whose driver record fuzzily matches to sarah fisher . take the laps record of this row . select the rows whose driver record fuzzily matches to dan wheldon . 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 ; driver ; sarah fisher } ; laps } ; hop { filter_eq { all_rows ; driver ; dan wheldon } ; laps } }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is january 11 .
Output: no
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; january 11 }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is january 11 .
Output: yes
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; january 11 }, interpretation: the sum of the enrollment record of all rows is 81233 .
Output: no
Command: round_eq { sum { all_rows ; enrollment } ; 81233 }, interpretation: the sum of the enrollment record of all rows is 81233 .
Output: yes
Command: round_eq { sum { all_rows ; enrollment } ; 81233 }, interpretation: the average of the finish position record of all rows is 42.8 .
Output: no
Command: round_eq { avg { all_rows ; finish position } ; 42.8 }, interpretation: the average of the finish position record of all rows is 42.8 .
Output: yes
Command: round_eq { avg { all_rows ; finish position } ; 42.8 }, interpretation: the average of the enrollment record of all rows is 20944 .
Output: no
Command: round_eq { avg { all_rows ; enrollment } ; 20944 }, interpretation: the average of the enrollment record of all rows is 20944 .
Output: yes
Command: round_eq { avg { all_rows ; enrollment } ; 20944 }, interpretation: select the rows whose season record is greater than or equal to 1973 . the sum of the won record of these rows is 77 .
Output: no
Command: round_eq { sum { filter_greater_eq { all_rows ; season ; 1973 } ; won } ; 77 }, interpretation: select the rows whose season record is greater than or equal to 1973 . the sum of the won record of these rows is 77 .
Output: yes
Command: round_eq { sum { filter_greater_eq { all_rows ; season ; 1973 } ; won } ; 77 }, interpretation: for the home team score records of all rows , most of them are greater than 10 .
Output: no
Command: most_greater { all_rows ; home team score ; 10 }, interpretation: for the home team score records of all rows , most of them are greater than 10 .
Output: yes
Command: most_greater { all_rows ; home team score ; 10 }, interpretation: select the row whose 2012 record of all rows is maximum . the location record of this row is lake buena vista , florida , usa .
Output: no
Command: eq { hop { argmax { all_rows ; 2012 } ; location } ; lake buena vista , florida , usa }, interpretation: select the row whose 2012 record of all rows is maximum . the location record of this row is lake buena vista , florida , usa .
Output: yes
Command: eq { hop { argmax { all_rows ; 2012 } ; location } ; lake buena vista , florida , usa }, interpretation: for the games played records of all rows , all of them are equal to 36 .
Output: no
Command: all_eq { all_rows ; games played ; 36 }, interpretation: for the points records of all rows , most of them are greater than or equal to 10 .
Output: no
Command: most_greater_eq { all_rows ; points ; 10 }, interpretation: for the points records of all rows , most of them are greater than or equal to 10 .
Output: yes
Command: most_greater_eq { all_rows ; points ; 10 }, interpretation: select the rows whose date record fuzzily matches to august . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; august } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to august . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; august } } ; 2 }, interpretation: select the rows whose playoffs record fuzzily matches to champion . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: no
Command: and { only { filter_eq { all_rows ; playoffs ; champion } } ; eq { hop { filter_eq { all_rows ; playoffs ; champion } ; year } ; 1989 } }, interpretation: select the rows whose playoffs record fuzzily matches to champion . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: yes
Command: and { only { filter_eq { all_rows ; playoffs ; champion } } ; eq { hop { filter_eq { all_rows ; playoffs ; champion } ; year } ; 1989 } }, interpretation: select the rows whose competition record fuzzily matches to friendly . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
Output: no
Command: and { only { filter_eq { all_rows ; competition ; friendly } } ; eq { hop { filter_eq { all_rows ; competition ; friendly } ; date } ; 15 august 2012 } }, interpretation: select the rows whose competition record fuzzily matches to friendly . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
Output: yes
Command: and { only { filter_eq { all_rows ; competition ; friendly } } ; eq { hop { filter_eq { all_rows ; competition ; friendly } ; date } ; 15 august 2012 } }, interpretation: select the rows whose date record fuzzily matches to august . the sum of the attendance record of these rows is 46879 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; august } ; attendance } ; 46879 }, interpretation: select the rows whose date record fuzzily matches to august . the sum of the attendance record of these rows is 46879 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; august } ; attendance } ; 46879 }, interpretation: select the rows whose date record fuzzily matches to september . the sum of the attendance record of these rows is 226814 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; september } ; attendance } ; 226814 }, interpretation: select the rows whose date record fuzzily matches to september . the sum of the attendance record of these rows is 226814 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; september } ; attendance } ; 226814 }, interpretation: select the rows whose air force cross record fuzzily matches to lifesaving medal . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; air force cross ; lifesaving medal } } ; 2 }, interpretation: select the rows whose air force cross record fuzzily matches to lifesaving medal . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; air force cross ; lifesaving medal } } ; 2 }, interpretation: select the row whose viewers record of all rows is maximum . the no record of this row is 1 .
Output: no
Command: eq { hop { argmax { all_rows ; viewers } ; no } ; 1 }, interpretation: select the row whose viewers record of all rows is maximum . the no record of this row is 1 .
Output: yes
Command: eq { hop { argmax { all_rows ; viewers } ; no } ; 1 }, interpretation: select the rows whose college name record fuzzily matches to government thiruvarur medical college . take the estd record of this row . select the rows whose college name record fuzzily matches to government theni medical college . take the estd record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; college name ; government thiruvarur medical college } ; estd } ; hop { filter_eq { all_rows ; college name ; government theni medical college } ; estd } }, interpretation: select the rows whose college name record fuzzily matches to government thiruvarur medical college . take the estd record of this row . select the rows whose college name record fuzzily matches to government theni medical college . take the estd record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; college name ; government thiruvarur medical college } ; estd } ; hop { filter_eq { all_rows ; college name ; government theni medical college } ; estd } }, interpretation: select the rows whose team record fuzzily matches to pescarolo sport . for the laps records of these rows , most of them are greater than 300 .
Output: no
Command: most_greater { filter_eq { all_rows ; team ; pescarolo sport } ; laps ; 300 }, interpretation: select the rows whose team record fuzzily matches to pescarolo sport . for the laps records of these rows , most of them are greater than 300 .
Output: yes
Command: most_greater { filter_eq { all_rows ; team ; pescarolo sport } ; laps ; 300 }, interpretation: the average of the points record of all rows is 263.16 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 263.16 }, interpretation: the average of the points record of all rows is 263.16 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 263.16 }, interpretation: select the row whose frequency mhz record of all rows is 2nd maximum . the city of license record of this row is pound , virginia .
Output: no
Command: eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; city of license } ; pound , virginia }, interpretation: select the row whose frequency mhz record of all rows is 2nd maximum . the city of license record of this row is pound , virginia .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; city of license } ; pound , virginia }, interpretation: for the reason for change records of all rows , most of them fuzzily match to died .
Output: no
Command: most_eq { all_rows ; reason for change ; died }, interpretation: for the reason for change records of all rows , most of them fuzzily match to died .
Output: yes
Command: most_eq { all_rows ; reason for change ; died }, interpretation: for the games records of all rows , most of them are equal to 5 .
Output: no
Command: most_eq { all_rows ; games ; 5 }, interpretation: for the games records of all rows , most of them are equal to 5 .
Output: yes
Command: most_eq { all_rows ; games ; 5 }, interpretation: select the rows whose competition record fuzzily matches to uefa cup . among these rows , select the rows whose result record fuzzily matches to draw . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; competition ; uefa cup } ; result ; draw } } ; 2 }, interpretation: select the rows whose competition record fuzzily matches to uefa cup . among these rows , select the rows whose result record fuzzily matches to draw . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; competition ; uefa cup } ; result ; draw } } ; 2 }, interpretation: select the row whose overall record of all rows is maximum . the name record of this row is sean renfree .
Output: no
Command: eq { hop { argmax { all_rows ; overall } ; name } ; sean renfree }, interpretation: select the row whose overall record of all rows is maximum . the name record of this row is sean renfree .
Output: yes
Command: eq { hop { argmax { all_rows ; overall } ; name } ; sean renfree }, interpretation: the average of the crowd record of all rows is 25000-27000 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 25000-27000 }, interpretation: the average of the crowd record of all rows is 25000-27000 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 25000-27000 }, interpretation: select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row . select the rows whose dance record fuzzily matches to jive . take the week record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week } ; hop { filter_eq { all_rows ; dance ; jive } ; week } }, interpretation: select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row . select the rows whose dance record fuzzily matches to jive . take the week record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week } ; hop { filter_eq { all_rows ; dance ; jive } ; week } }, interpretation: select the rows whose date record fuzzily matches to september . the average of the attendance record of these rows is 56,592 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; date ; september } ; attendance } ; 56,592 }, interpretation: select the rows whose date record fuzzily matches to september . the average of the attendance record of these rows is 56,592 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; date ; september } ; attendance } ; 56,592 }, interpretation: for the tyres records of all rows , most of them fuzzily match to g .
Output: no
Command: most_eq { all_rows ; tyres ; g }, interpretation: for the tyres records of all rows , most of them fuzzily match to g .
Output: yes
Command: most_eq { all_rows ; tyres ; g }, interpretation: for the apparent magnitude records of all rows , most of them are greater than 10.0 .
Output: no
Command: most_greater { all_rows ; apparent magnitude ; 10.0 }, interpretation: for the apparent magnitude records of all rows , most of them are greater than 10.0 .
Output: yes
Command: most_greater { all_rows ; apparent magnitude ; 10.0 }, interpretation: select the rows whose interregnum began record fuzzily matches to 20 february 1790 death of joseph ii . take the duration record of this row . select the rows whose interregnum began record fuzzily matches to 1 march 1792 death of leopold ii . take the duration record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; interregnum began ; 20 february 1790 death of joseph ii } ; duration } ; hop { filter_eq { all_rows ; interregnum began ; 1 march 1792 death of leopold ii } ; duration } }, interpretation: select the rows whose interregnum began record fuzzily matches to 20 february 1790 death of joseph ii . take the duration record of this row . select the rows whose interregnum began record fuzzily matches to 1 march 1792 death of leopold ii . take the duration record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; interregnum began ; 20 february 1790 death of joseph ii } ; duration } ; hop { filter_eq { all_rows ; interregnum began ; 1 march 1792 death of leopold ii } ; duration } }, interpretation: select the rows whose result record fuzzily matches to final . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; final } } ; 4 }, interpretation: select the rows whose result record fuzzily matches to final . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; final } } ; 4 }, interpretation: select the row whose votes record of all rows is 2nd maximum . the party record of this row is labour .
Output: no
Command: eq { hop { nth_argmax { all_rows ; votes ; 2 } ; party } ; labour }, interpretation: select the row whose votes record of all rows is 2nd maximum . the party record of this row is labour .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; votes ; 2 } ; party } ; labour }, interpretation: select the row whose margin record of all rows is maximum . the year record of this row is 1980 .
Output: no
Command: eq { hop { argmax { all_rows ; margin } ; year } ; 1980 }, interpretation: select the row whose margin record of all rows is maximum . the year record of this row is 1980 .
Output: yes
Command: eq { hop { argmax { all_rows ; margin } ; year } ; 1980 }, interpretation: select the rows whose accolade record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; accolade } } ; 6 }, interpretation: select the rows whose accolade record is arbitrary . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_all { all_rows ; accolade } } ; 6 }, interpretation: select the row whose evening gown record of all rows is minimum . the state record of this row is pennsylvania .
Output: no
Command: eq { hop { argmin { all_rows ; evening gown } ; state } ; pennsylvania }, interpretation: select the row whose evening gown record of all rows is minimum . the state record of this row is pennsylvania .
Output: yes
Command: eq { hop { argmin { all_rows ; evening gown } ; state } ; pennsylvania }, interpretation: select the row whose points record of all rows is maximum . the year record of this row is 1985 .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; year } ; 1985 }, interpretation: select the row whose points record of all rows is maximum . the year record of this row is 1985 .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; year } ; 1985 }, interpretation: select the row whose viewers ( millions ) record of all rows is maximum . the episode record of this row is 7 .
Output: no
Command: eq { hop { argmax { all_rows ; viewers ( millions ) } ; episode } ; 7 }, interpretation: select the row whose viewers ( millions ) record of all rows is maximum . the episode record of this row is 7 .
Output: yes
Command: eq { hop { argmax { all_rows ; viewers ( millions ) } ; episode } ; 7 }, interpretation: the average of the attendance record of all rows is 45585 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 45585 }, interpretation: the average of the attendance record of all rows is 45585 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 45585 }, interpretation: select the rows whose partner record fuzzily matches to simon aspelin . the number of such rows is 11 .
Output: no
Command: eq { count { filter_eq { all_rows ; partner ; simon aspelin } } ; 11 }, interpretation: select the rows whose partner record fuzzily matches to simon aspelin . the number of such rows is 11 .
Output: yes
Command: eq { count { filter_eq { all_rows ; partner ; simon aspelin } } ; 11 }, interpretation: the average of the candidates record of all rows is 13 .
Output: no
Command: round_eq { avg { all_rows ; candidates } ; 13 }, interpretation: the average of the candidates record of all rows is 13 .
Output: yes
Command: round_eq { avg { all_rows ; candidates } ; 13 }, interpretation: select the rows whose area ( km square ) record is greater than 1000000 . for the population ( millions , 2011 ) records of these rows , most of them are greater than 6 .
Output: no
Command: most_greater { filter_greater { all_rows ; area ( km square ) ; 1000000 } ; population ( millions , 2011 ) ; 6 }, interpretation: select the rows whose area ( km square ) record is greater than 1000000 . for the population ( millions , 2011 ) records of these rows , most of them are greater than 6 .
Output: yes
Command: most_greater { filter_greater { all_rows ; area ( km square ) ; 1000000 } ; population ( millions , 2011 ) ; 6 }, interpretation: the sum of the casinos record of all rows is 217 .
Output: no
Command: round_eq { sum { all_rows ; casinos } ; 217 }, interpretation: the sum of the casinos record of all rows is 217 .
Output: yes
Command: round_eq { sum { all_rows ; casinos } ; 217 }, interpretation: the average of the blackberry record of all rows is 3.19 % .
Output: no
Command: round_eq { avg { all_rows ; blackberry } ; 3.19 % }, interpretation: the average of the blackberry record of all rows is 3.19 % .
Output: yes
Command: round_eq { avg { all_rows ; blackberry } ; 3.19 % }, interpretation: the average of the attendance record of all rows is 43174 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 43174 }, interpretation: the average of the attendance record of all rows is 43174 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 43174 }, interpretation: select the rows whose date record fuzzily matches to 1997 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; 1997 } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 1997 . the number of such rows is 2 .
Output: yes