niryuu/sni-each-converted huggingface dataset:
Command: eq { hop { argmax { all_rows ; viewers ( millions ) } ; episode } ; episode 5 }, interpretation: select the row whose viewers ( millions ) record of all rows is maximum . the episode record of this row is episode 5 .
Output: yes
Command: eq { hop { argmax { all_rows ; viewers ( millions ) } ; episode } ; episode 5 }, interpretation: select the rows whose venue record fuzzily matches to antwerp . among these rows , select the rows whose winner record fuzzily matches to steve davis . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; venue ; antwerp } ; winner ; steve davis } } ; 1 }, interpretation: select the rows whose venue record fuzzily matches to antwerp . among these rows , select the rows whose winner record fuzzily matches to steve davis . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; venue ; antwerp } ; winner ; steve davis } } ; 1 }, interpretation: the sum of the attendance record of all rows is 554905 .
Output: no
Command: round_eq { sum { all_rows ; attendance } ; 554905 }, interpretation: the sum of the attendance record of all rows is 554905 .
Output: yes
Command: round_eq { sum { all_rows ; attendance } ; 554905 }, interpretation: select the rows whose pole record is equal to 0 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; pole ; 0 } } ; 4 }, interpretation: select the rows whose pole record is equal to 0 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; pole ; 0 } } ; 4 }, interpretation: for the round records of all rows , most of them are equal to 1 .
Output: no
Command: most_eq { all_rows ; round ; 1 }, interpretation: for the round records of all rows , most of them are equal to 1 .
Output: yes
Command: most_eq { all_rows ; round ; 1 }, interpretation: select the rows whose frequency record is less than 1000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { all_rows ; frequency ; 1000 } } ; 3 }, interpretation: select the rows whose frequency record is less than 1000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { all_rows ; frequency ; 1000 } } ; 3 }, interpretation: select the row whose erp w record of all rows is maximum . the call sign record of this row is w273ae .
Output: no
Command: eq { hop { argmax { all_rows ; erp w } ; call sign } ; w273ae }, interpretation: select the row whose erp w record of all rows is maximum . the call sign record of this row is w273ae .
Output: yes
Command: eq { hop { argmax { all_rows ; erp w } ; call sign } ; w273ae }, interpretation: the average of the to par record of all rows is 13.4 .
Output: no
Command: round_eq { avg { all_rows ; to par } ; 13.4 }, interpretation: the average of the to par record of all rows is 13.4 .
Output: yes
Command: round_eq { avg { all_rows ; to par } ; 13.4 }, interpretation: select the rows whose rank record fuzzily matches to 21st . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; rank ; 21st } } ; 3 }, interpretation: select the rows whose rank record fuzzily matches to 21st . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; rank ; 21st } } ; 3 }, interpretation: select the rows whose name record fuzzily matches to keith benson . take the weight record of this row . select the rows whose name record fuzzily matches to tim williams . take the weight record of this row . the first record is 10 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; keith benson } ; weight } ; hop { filter_eq { all_rows ; name ; tim williams } ; weight } } ; 10 }, interpretation: select the rows whose name record fuzzily matches to keith benson . take the weight record of this row . select the rows whose name record fuzzily matches to tim williams . take the weight record of this row . the first record is 10 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; keith benson } ; weight } ; hop { filter_eq { all_rows ; name ; tim williams } ; weight } } ; 10 }, interpretation: for the date records of all rows , all of them fuzzily match to 21 july 1951 .
Output: no
Command: all_eq { all_rows ; date ; 21 july 1951 }, interpretation: for the date records of all rows , all of them fuzzily match to 21 july 1951 .
Output: yes
Command: all_eq { all_rows ; date ; 21 july 1951 }, interpretation: select the rows whose country record fuzzily matches to ger . there is only one such row in the table . the name record of this unqiue row is bopp .
Output: no
Command: and { only { filter_eq { all_rows ; country ; ger } } ; eq { hop { filter_eq { all_rows ; country ; ger } ; name } ; bopp } }, interpretation: select the rows whose country record fuzzily matches to ger . there is only one such row in the table . the name record of this unqiue row is bopp .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; ger } } ; eq { hop { filter_eq { all_rows ; country ; ger } ; name } ; bopp } }, interpretation: select the row whose televote record of all rows is maximum . the song record of this row is dj , take me away .
Output: no
Command: eq { hop { argmax { all_rows ; televote } ; song } ; dj , take me away }, interpretation: select the row whose televote record of all rows is maximum . the song record of this row is dj , take me away .
Output: yes
Command: eq { hop { argmax { all_rows ; televote } ; song } ; dj , take me away }, interpretation: the average of the enrolment record of all rows is 831 .
Output: no
Command: round_eq { avg { all_rows ; enrolment } ; 831 }, interpretation: the average of the enrolment record of all rows is 831 .
Output: yes
Command: round_eq { avg { all_rows ; enrolment } ; 831 }, interpretation: select the rows whose artist record fuzzily matches to gorgoroth . there is only one such row in the table . the title record of this unqiue row is bergen 1996 .
Output: no
Command: and { only { filter_eq { all_rows ; artist ; gorgoroth } } ; eq { hop { filter_eq { all_rows ; artist ; gorgoroth } ; title } ; bergen 1996 } }, interpretation: select the rows whose artist record fuzzily matches to gorgoroth . there is only one such row in the table . the title record of this unqiue row is bergen 1996 .
Output: yes
Command: and { only { filter_eq { all_rows ; artist ; gorgoroth } } ; eq { hop { filter_eq { all_rows ; artist ; gorgoroth } ; title } ; bergen 1996 } }, interpretation: select the rows whose location record fuzzily matches to china . for the status records of these rows , all of them fuzzily match to valid .
Output: no
Command: all_eq { filter_eq { all_rows ; location ; china } ; status ; valid }, interpretation: select the rows whose location record fuzzily matches to china . for the status records of these rows , all of them fuzzily match to valid .
Output: yes
Command: all_eq { filter_eq { all_rows ; location ; china } ; status ; valid }, interpretation: select the row whose first elected record of all rows is minimum . the incumbent record of this row is john e rankin .
Output: no
Command: eq { hop { argmin { all_rows ; first elected } ; incumbent } ; john e rankin }, interpretation: select the row whose first elected record of all rows is minimum . the incumbent record of this row is john e rankin .
Output: yes
Command: eq { hop { argmin { all_rows ; first elected } ; incumbent } ; john e rankin }, interpretation: select the row whose 1 usd = record of all rows is maximum . the currency record of this row is paraguayan guaraní ( pyg ) .
Output: no
Command: eq { hop { argmax { all_rows ; 1 usd, interpretation: select the row whose 1 usd = record of all rows is maximum . the currency record of this row is paraguayan guaraní ( pyg ) .
Output: yes
Command: eq { hop { argmax { all_rows ; 1 usd, interpretation: select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table . the player record of this unqiue row is matthew anderson .
Output: no
Command: and { only { filter_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_eq { all_rows ; nationality ; united states } ; player } ; matthew anderson } }, interpretation: select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table . the player record of this unqiue row is matthew anderson .
Output: yes
Command: and { only { filter_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_eq { all_rows ; nationality ; united states } ; player } ; matthew anderson } }, interpretation: the average of the us viewers ( millions ) record of all rows is 0.944 .
Output: no
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 0.944 }, interpretation: the average of the us viewers ( millions ) record of all rows is 0.944 .
Output: yes
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 0.944 }, interpretation: select the rows whose runs scored record is equal to 313 . there is only one such row in the table . the name record of this unqiue row is adam gilchrist ( wk ) .
Output: no
Command: and { only { filter_eq { all_rows ; runs scored ; 313 } } ; eq { hop { filter_eq { all_rows ; runs scored ; 313 } ; name } ; adam gilchrist ( wk ) } }, interpretation: select the rows whose runs scored record is equal to 313 . there is only one such row in the table . the name record of this unqiue row is adam gilchrist ( wk ) .
Output: yes
Command: and { only { filter_eq { all_rows ; runs scored ; 313 } } ; eq { hop { filter_eq { all_rows ; runs scored ; 313 } ; name } ; adam gilchrist ( wk ) } }, interpretation: select the rows whose launcher record fuzzily matches to saturn v . there is only one such row in the table . the flights record of this unqiue row is 10 lun / or .
Output: no
Command: and { only { filter_eq { all_rows ; launcher ; saturn v } } ; eq { hop { filter_eq { all_rows ; launcher ; saturn v } ; flights } ; 10 lun / or } }, interpretation: select the rows whose launcher record fuzzily matches to saturn v . there is only one such row in the table . the flights record of this unqiue row is 10 lun / or .
Output: yes
Command: and { only { filter_eq { all_rows ; launcher ; saturn v } } ; eq { hop { filter_eq { all_rows ; launcher ; saturn v } ; flights } ; 10 lun / or } }, interpretation: select the rows whose agg record fuzzily matches to 2-1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; agg ; 2-1 } } ; 2 }, interpretation: select the rows whose agg record fuzzily matches to 2-1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; agg ; 2-1 } } ; 2 }, interpretation: select the rows whose 1st leg record fuzzily matches to 1 - 1 . among these rows , select the rows whose 2nd leg record fuzzily matches to 1 - 1 . there is only one such row in the table . the team 1 record of this unqiue row is atlético mineiro .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; 2nd leg ; 1 - 1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; 2nd leg ; 1 - 1 } ; team 1 } ; atlético mineiro } }, interpretation: select the rows whose 1st leg record fuzzily matches to 1 - 1 . among these rows , select the rows whose 2nd leg record fuzzily matches to 1 - 1 . there is only one such row in the table . the team 1 record of this unqiue row is atlético mineiro .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; 2nd leg ; 1 - 1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; 2nd leg ; 1 - 1 } ; team 1 } ; atlético mineiro } }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park }, interpretation: select the rows whose opponent record fuzzily matches to tampa bay buccaneers . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }, interpretation: select the rows whose opponent record fuzzily matches to tampa bay buccaneers . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }, interpretation: select the rows whose points record is equal to 16 . there is only one such row in the table . the team record of this unqiue row is ec são caetano .
Output: no
Command: and { only { filter_eq { all_rows ; points ; 16 } } ; eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano } }, interpretation: select the rows whose points record is equal to 16 . there is only one such row in the table . the team record of this unqiue row is ec são caetano .
Output: yes
Command: and { only { filter_eq { all_rows ; points ; 16 } } ; eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano } }, interpretation: select the row whose points record of all rows is maximum . the game record of this row is 10 .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; game } ; 10 }, interpretation: select the row whose points record of all rows is maximum . the game record of this row is 10 .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; game } ; 10 }, interpretation: the sum of the attendance record of all rows is 764271 .
Output: no
Command: round_eq { sum { all_rows ; attendance } ; 764271 }, interpretation: the sum of the attendance record of all rows is 764271 .
Output: yes
Command: round_eq { sum { all_rows ; attendance } ; 764271 }, interpretation: select the rows whose partner record fuzzily matches to sergio galdós . there is only one such row in the table . the tournament record of this unqiue row is panama city .
Output: no
Command: and { only { filter_eq { all_rows ; partner ; sergio galdós } } ; eq { hop { filter_eq { all_rows ; partner ; sergio galdós } ; tournament } ; panama city } }, interpretation: select the rows whose partner record fuzzily matches to sergio galdós . there is only one such row in the table . the tournament record of this unqiue row is panama city .
Output: yes
Command: and { only { filter_eq { all_rows ; partner ; sergio galdós } } ; eq { hop { filter_eq { all_rows ; partner ; sergio galdós } ; tournament } ; panama city } }, interpretation: select the rows whose type record fuzzily matches to transferred . among these rows , select the rows whose transfer fee record fuzzily matches to undisclosed . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; type ; transferred } ; transfer fee ; undisclosed } } ; 3 }, interpretation: select the rows whose type record fuzzily matches to transferred . among these rows , select the rows whose transfer fee record fuzzily matches to undisclosed . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; type ; transferred } ; transfer fee ; undisclosed } } ; 3 }, interpretation: select the rows whose tribunal record fuzzily matches to coimbra . take the executions in persona record of this row . select the rows whose tribunal record fuzzily matches to goa . take the executions in persona record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; tribunal ; coimbra } ; executions in persona } ; hop { filter_eq { all_rows ; tribunal ; goa } ; executions in persona } }, interpretation: select the rows whose tribunal record fuzzily matches to coimbra . take the executions in persona record of this row . select the rows whose tribunal record fuzzily matches to goa . take the executions in persona record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; tribunal ; coimbra } ; executions in persona } ; hop { filter_eq { all_rows ; tribunal ; goa } ; executions in persona } }, interpretation: select the rows whose opposing teams record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing teams record fuzzily matches to france . 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 ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing teams ; france } ; against } }, interpretation: select the rows whose opposing teams record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing teams record fuzzily matches to france . 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 ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing teams ; france } ; against } }, interpretation: for the elevation ( m ) records of all rows , all of them are greater than 1000 .
Output: no
Command: all_greater { all_rows ; elevation ( m ) ; 1000 }, interpretation: for the elevation ( m ) records of all rows , all of them are greater than 1000 .
Output: yes
Command: all_greater { all_rows ; elevation ( m ) ; 1000 }, interpretation: for the start records of all rows , most of them are equal to 3 .
Output: no
Command: most_eq { all_rows ; start ; 3 }, interpretation: for the start records of all rows , most of them are equal to 3 .
Output: yes
Command: most_eq { all_rows ; start ; 3 }, interpretation: select the rows whose gdp per capita ( us ) record is less than 16000 . there is only one such row in the table . the member countries record of this unqiue row is finland .
Output: no
Command: and { only { filter_less { all_rows ; gdp per capita ( us ) ; 16000 } } ; eq { hop { filter_less { all_rows ; gdp per capita ( us ) ; 16000 } ; member countries } ; finland } }, interpretation: select the rows whose gdp per capita ( us ) record is less than 16000 . there is only one such row in the table . the member countries record of this unqiue row is finland .
Output: yes
Command: and { only { filter_less { all_rows ; gdp per capita ( us ) ; 16000 } } ; eq { hop { filter_less { all_rows ; gdp per capita ( us ) ; 16000 } ; member countries } ; finland } }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is dowlais rfc .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; club } ; dowlais rfc }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is dowlais rfc .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; club } ; dowlais rfc }, interpretation: the 17th minimum year record of all rows is 2005 . the award record of the row with 17th minimum year record is london critics circle film awards . the nominated work record of the row with 17th minimum year record is maria full of grace .
Output: no
Command: and { eq { nth_min { all_rows ; year ; 17 } ; 2005 } ; and { eq { hop { nth_argmin { all_rows ; year ; 17 } ; award } ; london critics circle film awards } ; eq { hop { nth_argmin { all_rows ; year ; 17 } ; nominated work } ; maria full of grace } } }, interpretation: the 17th minimum year record of all rows is 2005 . the award record of the row with 17th minimum year record is london critics circle film awards . the nominated work record of the row with 17th minimum year record is maria full of grace .
Output: yes
Command: and { eq { nth_min { all_rows ; year ; 17 } ; 2005 } ; and { eq { hop { nth_argmin { all_rows ; year ; 17 } ; award } ; london critics circle film awards } ; eq { hop { nth_argmin { all_rows ; year ; 17 } ; nominated work } ; maria full of grace } } }, interpretation: the average of the points record of all rows is 111 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 111 }, interpretation: the average of the points record of all rows is 111 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 111 }, interpretation: select the rows whose national cup record fuzzily matches to turkish basketball cup . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; national cup ; turkish basketball cup } } ; 2 }, interpretation: select the rows whose national cup record fuzzily matches to turkish basketball cup . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; national cup ; turkish basketball cup } } ; 2 }, interpretation: select the rows whose college record fuzzily matches to eckerd . there is only one such row in the table . the player record of this unqiue row is jay washington .
Output: no
Command: and { only { filter_eq { all_rows ; college ; eckerd } } ; eq { hop { filter_eq { all_rows ; college ; eckerd } ; player } ; jay washington } }, interpretation: select the rows whose college record fuzzily matches to eckerd . there is only one such row in the table . the player record of this unqiue row is jay washington .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; eckerd } } ; eq { hop { filter_eq { all_rows ; college ; eckerd } ; player } ; jay washington } }, interpretation: select the rows whose institution record is arbitrary . the number of such rows is 11 .
Output: no
Command: eq { count { filter_all { all_rows ; institution } } ; 11 }, interpretation: select the rows whose institution record is arbitrary . the number of such rows is 11 .
Output: yes
Command: eq { count { filter_all { all_rows ; institution } } ; 11 }, interpretation: select the rows whose team record fuzzily matches to new jersey . the 1st minimum date record of these rows is october 5 .
Output: no
Command: eq { nth_min { filter_eq { all_rows ; team ; new jersey } ; date ; 1 } ; october 5 }, interpretation: select the rows whose team record fuzzily matches to new jersey . the 1st minimum date record of these rows is october 5 .
Output: yes
Command: eq { nth_min { filter_eq { all_rows ; team ; new jersey } ; date ; 1 } ; october 5 }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is princes park .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; princes park }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is princes park .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; princes park }, interpretation: select the row whose transfer fee record of all rows is maximum . the name record of this row is becchio .
Output: no
Command: eq { hop { argmax { all_rows ; transfer fee } ; name } ; becchio }, interpretation: select the row whose transfer fee record of all rows is maximum . the name record of this row is becchio .
Output: yes
Command: eq { hop { argmax { all_rows ; transfer fee } ; name } ; becchio }, interpretation: select the row whose max altitude ( miles ) record of all rows is maximum . the pilot record of this row is joseph a walker .
Output: no
Command: eq { hop { argmax { all_rows ; max altitude ( miles ) } ; pilot } ; joseph a walker }, interpretation: select the row whose max altitude ( miles ) record of all rows is maximum . the pilot record of this row is joseph a walker .
Output: yes
Command: eq { hop { argmax { all_rows ; max altitude ( miles ) } ; pilot } ; joseph a walker }, interpretation: select the rows whose date record fuzzily matches to apr 2003 . the sum of the time record of these rows is 3:00 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; apr 2003 } ; time } ; 3:00 }, interpretation: select the rows whose date record fuzzily matches to apr 2003 . the sum of the time record of these rows is 3:00 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; apr 2003 } ; time } ; 3:00 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is november 13 , 1994 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; november 13 , 1994 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is november 13 , 1994 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; november 13 , 1994 }, interpretation: select the rows whose name record fuzzily matches to prudential tower . take the floors record of this row . select the rows whose name record fuzzily matches to hancock place . take the floors record of this row . the second record is 8 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; prudential tower } ; floors } ; hop { filter_eq { all_rows ; name ; hancock place } ; floors } } ; -8 }, interpretation: select the rows whose name record fuzzily matches to prudential tower . take the floors record of this row . select the rows whose name record fuzzily matches to hancock place . take the floors record of this row . the second record is 8 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; prudential tower } ; floors } ; hop { filter_eq { all_rows ; name ; hancock place } ; floors } } ; -8 }, interpretation: select the rows whose college record fuzzily matches to weber state . there is only one such row in the table . the player record of this unqiue row is lee white .
Output: no
Command: and { only { filter_eq { all_rows ; college ; weber state } } ; eq { hop { filter_eq { all_rows ; college ; weber state } ; player } ; lee white } }, interpretation: select the rows whose college record fuzzily matches to weber state . there is only one such row in the table . the player record of this unqiue row is lee white .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; weber state } } ; eq { hop { filter_eq { all_rows ; college ; weber state } ; player } ; lee white } }, interpretation: select the rows whose original air date record is arbitrary . the number of such rows is 9 .
Output: no
Command: eq { count { filter_all { all_rows ; original air date } } ; 9 }, interpretation: select the rows whose original air date record is arbitrary . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_all { all_rows ; original air date } } ; 9 }, interpretation: select the rows whose last appearance record fuzzily matches to today is life . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; last appearance ; today is life } } ; 8 }, interpretation: select the rows whose last appearance record fuzzily matches to today is life . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; last appearance ; today is life } } ; 8 }, interpretation: select the row whose points record of all rows is maximum . the year record of this row is 2007 - 08 .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; year } ; 2007 - 08 }, interpretation: select the row whose points record of all rows is maximum . the year record of this row is 2007 - 08 .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; year } ; 2007 - 08 }, interpretation: the average of the 2001 - 02 record of all rows is 2907 .
Output: no
Command: round_eq { avg { all_rows ; 2001 - 02 } ; 2907 }, interpretation: the average of the 2001 - 02 record of all rows is 2907 .
Output: yes
Command: round_eq { avg { all_rows ; 2001 - 02 } ; 2907 }, interpretation: select the rows whose ceased to be queen record fuzzily matches to divorced . there is only one such row in the table . the father record of this unqiue row is leopold vi , duke of austria .
Output: no
Command: and { only { filter_eq { all_rows ; ceased to be queen ; divorced } } ; eq { hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father } ; leopold vi , duke of austria } }, interpretation: select the rows whose ceased to be queen record fuzzily matches to divorced . there is only one such row in the table . the father record of this unqiue row is leopold vi , duke of austria .
Output: yes
Command: and { only { filter_eq { all_rows ; ceased to be queen ; divorced } } ; eq { hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father } ; leopold vi , duke of austria } }, interpretation: select the rows whose opponent record fuzzily matches to new york giants . the sum of the attendance record of these rows is 144706 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; opponent ; new york giants } ; attendance } ; 144706 }, interpretation: select the rows whose opponent record fuzzily matches to new york giants . the sum of the attendance record of these rows is 144706 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; opponent ; new york giants } ; attendance } ; 144706 }, interpretation: select the rows whose location record fuzzily matches to norfolk . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; location ; norfolk } } ; 4 }, interpretation: select the rows whose location record fuzzily matches to norfolk . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location ; norfolk } } ; 4 }, interpretation: select the rows whose goalkeeper record fuzzily matches to matías garavano . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to álvaro campos . take the goals record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; goalkeeper ; matías garavano } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; álvaro campos } ; goals } }, interpretation: select the rows whose goalkeeper record fuzzily matches to matías garavano . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to álvaro campos . take the goals record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; goalkeeper ; matías garavano } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; álvaro campos } ; goals } }, interpretation: select the row whose score record of all rows is minimum . the player record of this row is tim simpson .
Output: no
Command: eq { hop { argmin { all_rows ; score } ; player } ; tim simpson }, interpretation: select the row whose score record of all rows is minimum . the player record of this row is tim simpson .
Output: yes
Command: eq { hop { argmin { all_rows ; score } ; player } ; tim simpson }, interpretation: select the rows whose season outcome record fuzzily matches to won div ii state championship . there is only one such row in the table . the team record of this unqiue row is indians .
Output: no
Command: and { only { filter_eq { all_rows ; season outcome ; won div ii state championship } } ; eq { hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team } ; indians } }, interpretation: select the rows whose season outcome record fuzzily matches to won div ii state championship . there is only one such row in the table . the team record of this unqiue row is indians .
Output: yes
Command: and { only { filter_eq { all_rows ; season outcome ; won div ii state championship } } ; eq { hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team } ; indians } }, interpretation: for the type records of all rows , most of them fuzzily match to mountain .
Output: no
Command: most_eq { all_rows ; type ; mountain }, interpretation: for the type records of all rows , most of them fuzzily match to mountain .
Output: yes
Command: most_eq { all_rows ; type ; mountain }, interpretation: select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } } ; 4 }, interpretation: select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } } ; 4 }, interpretation: select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 4 }, interpretation: select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 4 }, interpretation: select the row whose time record of all rows is 2nd minimum . the winner record of this row is paddy o'prado .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 2 } ; winner } ; paddy o'prado }, interpretation: select the row whose time record of all rows is 2nd minimum . the winner record of this row is paddy o'prado .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 2 } ; winner } ; paddy o'prado }, interpretation: select the rows whose country record fuzzily matches to ch . there is only one such row in the table . the building record of this unqiue row is victoria hall .
Output: no
Command: and { only { filter_eq { all_rows ; country ; ch } } ; eq { hop { filter_eq { all_rows ; country ; ch } ; building } ; victoria hall } }, interpretation: select the rows whose country record fuzzily matches to ch . there is only one such row in the table . the building record of this unqiue row is victoria hall .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; ch } } ; eq { hop { filter_eq { all_rows ; country ; ch } ; building } ; victoria hall } }, interpretation: select the row whose capacity record of all rows is maximum . the team record of this row is dinamo zagreb .
Output: no
Command: eq { hop { argmax { all_rows ; capacity } ; team } ; dinamo zagreb }, interpretation: select the row whose capacity record of all rows is maximum . the team record of this row is dinamo zagreb .
Output: yes
Command: eq { hop { argmax { all_rows ; capacity } ; team } ; dinamo zagreb }, interpretation: select the rows whose location attendance record fuzzily matches to arizona veterans memorial coliseum . the sum of the score record of these rows is 200 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; location attendance ; arizona veterans memorial coliseum } ; score } ; 200 }, interpretation: select the rows whose location attendance record fuzzily matches to arizona veterans memorial coliseum . the sum of the score record of these rows is 200 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; location attendance ; arizona veterans memorial coliseum } ; score } ; 200 }, interpretation: the maximum drawn record of all rows is 2 .
Output: no
Command: eq { max { all_rows ; drawn } ; 2 }, interpretation: the maximum drawn record of all rows is 2 .
Output: yes
Command: eq { max { all_rows ; drawn } ; 2 }, interpretation: the average of the attendance record of all rows is 57797 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 57797 }, interpretation: the average of the attendance record of all rows is 57797 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 57797 }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .
Output: no
Command: all_eq { filter_eq { all_rows ; date ; october } ; result ; w }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .
Output: yes
Command: all_eq { filter_eq { all_rows ; date ; october } ; result ; w }, interpretation: select the rows whose competition record fuzzily matches to olympic trials . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; competition ; olympic trials } } ; 5 }, interpretation: select the rows whose competition record fuzzily matches to olympic trials . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; competition ; olympic trials } } ; 5 }, interpretation: select the rows whose number record is equal to 4 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; number ; 4 } } ; 2 }, interpretation: select the rows whose number record is equal to 4 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; number ; 4 } } ; 2 }, interpretation: select the row whose podiums record of all rows is maximum . the points record of this row is 357 .
Output: no
Command: eq { hop { argmax { all_rows ; podiums } ; points } ; 357 }, interpretation: select the row whose podiums record of all rows is maximum . the points record of this row is 357 .
Output: yes
Command: eq { hop { argmax { all_rows ; podiums } ; points } ; 357 }, interpretation: the average of the wins record of all rows is 2.2 .
Output: no
Command: round_eq { avg { all_rows ; wins } ; 2.2 }, interpretation: the average of the wins record of all rows is 2.2 .
Output: yes
Command: round_eq { avg { all_rows ; wins } ; 2.2 }, interpretation: select the rows whose first episode record fuzzily matches to golden parachute . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; first episode ; golden parachute } } ; 6 }, interpretation: select the rows whose first episode record fuzzily matches to golden parachute . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first episode ; golden parachute } } ; 6 }, interpretation: select the rows whose nation record fuzzily matches to lebanon . take the total record of this row . select the rows whose nation record fuzzily matches to french community of belgium . take the total record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nation ; lebanon } ; total } ; hop { filter_eq { all_rows ; nation ; french community of belgium } ; total } }, interpretation: select the rows whose nation record fuzzily matches to lebanon . take the total record of this row . select the rows whose nation record fuzzily matches to french community of belgium . take the total record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nation ; lebanon } ; total } ; hop { filter_eq { all_rows ; nation ; french community of belgium } ; total } }, interpretation: select the rows whose tournament record fuzzily matches to australia . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; australia } } ; 5 }, interpretation: select the rows whose tournament record fuzzily matches to australia . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; australia } } ; 5 }, interpretation: select the rows whose peak record fuzzily matches to mount tabwemasana . take the elevation ( m ) record of this row . select the rows whose peak record fuzzily matches to mount veve . take the elevation ( m ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; peak ; mount tabwemasana } ; elevation ( m ) } ; hop { filter_eq { all_rows ; peak ; mount veve } ; elevation ( m ) } }, interpretation: select the rows whose peak record fuzzily matches to mount tabwemasana . take the elevation ( m ) record of this row . select the rows whose peak record fuzzily matches to mount veve . take the elevation ( m ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; peak ; mount tabwemasana } ; elevation ( m ) } ; hop { filter_eq { all_rows ; peak ; mount veve } ; elevation ( m ) } }, interpretation: select the rows whose reg gp record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is rob mcvicar .
Output: no
Command: and { only { filter_eq { all_rows ; reg gp ; 1 } } ; eq { hop { filter_eq { all_rows ; reg gp ; 1 } ; player } ; rob mcvicar } }, interpretation: select the rows whose reg gp record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is rob mcvicar .
Output: yes
Command: and { only { filter_eq { all_rows ; reg gp ; 1 } } ; eq { hop { filter_eq { all_rows ; reg gp ; 1 } ; player } ; rob mcvicar } }, interpretation: select the rows whose storyteller record fuzzily matches to gary . there is only one such row in the table . the - record of this unqiue row is 11 .
Output: no
Command: and { only { filter_eq { all_rows ; storyteller ; gary } } ; eq { hop { filter_eq { all_rows ; storyteller ; gary } ; - } ; 11 } }, interpretation: select the rows whose storyteller record fuzzily matches to gary . there is only one such row in the table . the - record of this unqiue row is 11 .
Output: yes
Command: and { only { filter_eq { all_rows ; storyteller ; gary } } ; eq { hop { filter_eq { all_rows ; storyteller ; gary } ; - } ; 11 } }, interpretation: select the row whose date record of all rows is 5th minimum . the venue record of this row is guangzhou .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 5 } ; venue } ; guangzhou }, interpretation: select the row whose date record of all rows is 5th minimum . the venue record of this row is guangzhou .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 5 } ; venue } ; guangzhou }, interpretation: select the rows whose bowling record fuzzily matches to 17 - 48 . there is only one such row in the table . the player record of this unqiue row is colin blythe .
Output: no
Command: and { only { filter_eq { all_rows ; bowling ; 17 - 48 } } ; eq { hop { filter_eq { all_rows ; bowling ; 17 - 48 } ; player } ; colin blythe } }, interpretation: select the rows whose bowling record fuzzily matches to 17 - 48 . there is only one such row in the table . the player record of this unqiue row is colin blythe .
Output: yes
Command: and { only { filter_eq { all_rows ; bowling ; 17 - 48 } } ; eq { hop { filter_eq { all_rows ; bowling ; 17 - 48 } ; player } ; colin blythe } }, interpretation: for the country of origin records of all rows , most of them fuzzily match to philippines .
Output: no
Command: most_eq { all_rows ; country of origin ; philippines }, interpretation: for the country of origin records of all rows , most of them fuzzily match to philippines .
Output: yes
Command: most_eq { all_rows ; country of origin ; philippines }, interpretation: select the rows whose gold record is greater than or equal to 10 . among these rows , select the rows whose silver record is greater than or equal to 20 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater_eq { filter_greater_eq { all_rows ; gold ; 10 } ; silver ; 20 } } ; 3 }, interpretation: select the rows whose gold record is greater than or equal to 10 . among these rows , select the rows whose silver record is greater than or equal to 20 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater_eq { filter_greater_eq { all_rows ; gold ; 10 } ; silver ; 20 } } ; 3 }, interpretation: the average of the attendance record of all rows is 2270 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 2270 }, interpretation: the average of the attendance record of all rows is 2270 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 2270 }, interpretation: select the row whose games record of all rows is 2nd maximum . the player record of this row is david gonzalvez .
Output: no
Command: eq { hop { nth_argmax { all_rows ; games ; 2 } ; player } ; david gonzalvez }, interpretation: select the row whose games record of all rows is 2nd maximum . the player record of this row is david gonzalvez .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; games ; 2 } ; player } ; david gonzalvez }, interpretation: select the rows whose time / retired record fuzzily matches to accident . there is only one such row in the table . the driver record of this unqiue row is nino farina .
Output: no
Command: and { only { filter_eq { all_rows ; time / retired ; accident } } ; eq { hop { filter_eq { all_rows ; time / retired ; accident } ; driver } ; nino farina } }, interpretation: select the rows whose time / retired record fuzzily matches to accident . there is only one such row in the table . the driver record of this unqiue row is nino farina .
Output: yes
Command: and { only { filter_eq { all_rows ; time / retired ; accident } } ; eq { hop { filter_eq { all_rows ; time / retired ; accident } ; driver } ; nino farina } }, interpretation: select the row whose average record of all rows is maximum . the player record of this row is alec bedser .
Output: no
Command: eq { hop { argmax { all_rows ; average } ; player } ; alec bedser }, interpretation: select the row whose average record of all rows is maximum . the player record of this row is alec bedser .
Output: yes
Command: eq { hop { argmax { all_rows ; average } ; player } ; alec bedser }, interpretation: select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is geoff ogilvy .
Output: no
Command: and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; geoff ogilvy } }, interpretation: select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is geoff ogilvy .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; geoff ogilvy } }, interpretation: select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; television service ; telemarket } } ; 2 }, interpretation: select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; television service ; telemarket } } ; 2 }, interpretation: for the affiliation records of all rows , most of them fuzzily match to public .
Output: no
Command: most_eq { all_rows ; affiliation ; public }, interpretation: for the affiliation records of all rows , most of them fuzzily match to public .
Output: yes
Command: most_eq { all_rows ; affiliation ; public }, interpretation: select the row whose attendance record of all rows is 3rd maximum . the competition record of this row is danish superliga 2005 - 06 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; competition } ; danish superliga 2005 - 06 }, interpretation: select the row whose attendance record of all rows is 3rd maximum . the competition record of this row is danish superliga 2005 - 06 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; competition } ; danish superliga 2005 - 06 }, interpretation: select the rows whose time record fuzzily matches to +40 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; time ; +40 } } ; 2 }, interpretation: select the rows whose time record fuzzily matches to +40 . the number of such rows is 2 .
Output: yes