niryuu/sni-each-converted huggingface dataset:
Command: most_greater { all_rows ; wins ; 10 }, interpretation: select the row whose react record of all rows is 5th minimum . the name record of this row is christopher williams .
Output: no
Command: eq { hop { nth_argmin { all_rows ; react ; 5 } ; name } ; christopher williams }, interpretation: select the row whose react record of all rows is 5th minimum . the name record of this row is christopher williams .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; react ; 5 } ; name } ; christopher williams }, interpretation: for the visitor records of all rows , most of them fuzzily match to san antonio spurs .
Output: no
Command: most_eq { all_rows ; visitor ; san antonio spurs }, interpretation: for the visitor records of all rows , most of them fuzzily match to san antonio spurs .
Output: yes
Command: most_eq { all_rows ; visitor ; san antonio spurs }, interpretation: select the rows whose tournament record fuzzily matches to paris . take the career win - loss record of this row . select the rows whose tournament record fuzzily matches to rome . take the career win - loss record of this row . the first record does not match to the second record . the career win - loss record of the first row is 6 - 4 . the career win - loss record of the second row is 0 - 8 .
Output: no
Command: and { not_eq { hop { filter_eq { all_rows ; tournament ; paris } ; career win - loss } ; hop { filter_eq { all_rows ; tournament ; rome } ; career win - loss } } ; and { eq { hop { filter_eq { all_rows ; tournament ; paris } ; career win - loss } ; 6 - 4 } ; eq { hop { filter_eq { all_rows ; tournament ; rome } ; career win - loss } ; 0 - 8 } } }, interpretation: select the rows whose tournament record fuzzily matches to paris . take the career win - loss record of this row . select the rows whose tournament record fuzzily matches to rome . take the career win - loss record of this row . the first record does not match to the second record . the career win - loss record of the first row is 6 - 4 . the career win - loss record of the second row is 0 - 8 .
Output: yes
Command: and { not_eq { hop { filter_eq { all_rows ; tournament ; paris } ; career win - loss } ; hop { filter_eq { all_rows ; tournament ; rome } ; career win - loss } } ; and { eq { hop { filter_eq { all_rows ; tournament ; paris } ; career win - loss } ; 6 - 4 } ; eq { hop { filter_eq { all_rows ; tournament ; rome } ; career win - loss } ; 0 - 8 } } }, interpretation: select the rows whose style record fuzzily matches to country - western two - step . there is only one such row in the table . the couple record of this unqiue row is kherington payne mark kanemura .
Output: no
Command: and { only { filter_eq { all_rows ; style ; country - western two - step } } ; eq { hop { filter_eq { all_rows ; style ; country - western two - step } ; couple } ; kherington payne mark kanemura } }, interpretation: select the rows whose style record fuzzily matches to country - western two - step . there is only one such row in the table . the couple record of this unqiue row is kherington payne mark kanemura .
Output: yes
Command: and { only { filter_eq { all_rows ; style ; country - western two - step } } ; eq { hop { filter_eq { all_rows ; style ; country - western two - step } ; couple } ; kherington payne mark kanemura } }, interpretation: the average of the capacity record of all rows is 8561 .
Output: no
Command: round_eq { avg { all_rows ; capacity } ; 8561 }, interpretation: the average of the capacity record of all rows is 8561 .
Output: yes
Command: round_eq { avg { all_rows ; capacity } ; 8561 }, interpretation: the average of the erp / power w record of all rows is 6012 .
Output: no
Command: round_eq { avg { all_rows ; erp / power w } ; 6012 }, interpretation: the average of the erp / power w record of all rows is 6012 .
Output: yes
Command: round_eq { avg { all_rows ; erp / power w } ; 6012 }, interpretation: for the us viewers ( millions ) records of all rows , most of them fuzzily match to n / a .
Output: no
Command: most_eq { all_rows ; us viewers ( millions ) ; n / a }, interpretation: for the us viewers ( millions ) records of all rows , most of them fuzzily match to n / a .
Output: yes
Command: most_eq { all_rows ; us viewers ( millions ) ; n / a }, interpretation: select the rows whose ostrich record fuzzily matches to egyptian goose . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; ostrich ; egyptian goose } }, interpretation: select the rows whose ostrich record fuzzily matches to egyptian goose . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; ostrich ; egyptian goose } }, interpretation: for the date records of all rows , most of them fuzzily match to 25 october .
Output: no
Command: most_eq { all_rows ; date ; 25 october }, interpretation: for the date records of all rows , most of them fuzzily match to 25 october .
Output: yes
Command: most_eq { all_rows ; date ; 25 october }, interpretation: for the attendance records of all rows , most of them are greater than 15800 .
Output: no
Command: most_greater { all_rows ; attendance ; 15800 }, interpretation: for the attendance records of all rows , most of them are greater than 15800 .
Output: yes
Command: most_greater { all_rows ; attendance ; 15800 }, interpretation: select the rows whose points record is greater than or equal to 40 . for the difference records of these rows , all of them are greater than 40 .
Output: no
Command: all_greater { filter_greater_eq { all_rows ; points ; 40 } ; difference ; 40 }, interpretation: select the rows whose points record is greater than or equal to 40 . for the difference records of these rows , all of them are greater than 40 .
Output: yes
Command: all_greater { filter_greater_eq { all_rows ; points ; 40 } ; difference ; 40 }, interpretation: the average of the attendance record of all rows is 45,972 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 45,972 }, interpretation: the average of the attendance record of all rows is 45,972 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 45,972 }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is january 30 .
Output: no
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; january 30 }, interpretation: select the row whose location attendance record of all rows is maximum . the date record of this row is january 30 .
Output: yes
Command: eq { hop { argmax { all_rows ; location attendance } ; date } ; january 30 }, interpretation: select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 } } ; 3 }, interpretation: select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 } } ; 3 }, interpretation: select the rows whose constructor record fuzzily matches to talbot - lago - talbot . among these rows , select the rows whose laps record is greater than 30 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; constructor ; talbot - lago - talbot } ; laps ; 30 } } ; 5 }, interpretation: select the rows whose constructor record fuzzily matches to talbot - lago - talbot . among these rows , select the rows whose laps record is greater than 30 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; constructor ; talbot - lago - talbot } ; laps ; 30 } } ; 5 }, interpretation: for the type records of all rows , most of them fuzzily match to norteño .
Output: no
Command: most_eq { all_rows ; type ; norteño }, interpretation: for the type records of all rows , most of them fuzzily match to norteño .
Output: yes
Command: most_eq { all_rows ; type ; norteño }, interpretation: select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams - renault . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams - renault . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault } } ; 2 }, interpretation: for the country records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; country ; united states }, interpretation: select the row whose central rate record of all rows is maximum . the currency record of this row is danish krone .
Output: no
Command: eq { hop { argmax { all_rows ; central rate } ; currency } ; danish krone }, interpretation: select the row whose central rate record of all rows is maximum . the currency record of this row is danish krone .
Output: yes
Command: eq { hop { argmax { all_rows ; central rate } ; currency } ; danish krone }, interpretation: the average of the points record of all rows is 265 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 265 }, interpretation: the average of the points record of all rows is 265 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 265 }, interpretation: select the rows whose district record fuzzily matches to shajapur . there is only one such row in the table . the name record of this unqiue row is susner .
Output: no
Command: and { only { filter_eq { all_rows ; district ; shajapur } } ; eq { hop { filter_eq { all_rows ; district ; shajapur } ; name } ; susner } }, interpretation: select the rows whose district record fuzzily matches to shajapur . there is only one such row in the table . the name record of this unqiue row is susner .
Output: yes
Command: and { only { filter_eq { all_rows ; district ; shajapur } } ; eq { hop { filter_eq { all_rows ; district ; shajapur } ; name } ; susner } }, interpretation: select the rows whose year joined record is equal to 1971 . there is only one such row in the table . the school record of this unqiue row is southern wells .
Output: no
Command: and { only { filter_eq { all_rows ; year joined ; 1971 } } ; eq { hop { filter_eq { all_rows ; year joined ; 1971 } ; school } ; southern wells } }, interpretation: select the rows whose year joined record is equal to 1971 . there is only one such row in the table . the school record of this unqiue row is southern wells .
Output: yes
Command: and { only { filter_eq { all_rows ; year joined ; 1971 } } ; eq { hop { filter_eq { all_rows ; year joined ; 1971 } ; school } ; southern wells } }, interpretation: select the row whose jersey number ( s ) record of all rows is 2nd maximum . the player record of this row is patrick ewing .
Output: no
Command: eq { hop { nth_argmax { all_rows ; jersey number ( s ) ; 2 } ; player } ; patrick ewing }, interpretation: select the row whose jersey number ( s ) record of all rows is 2nd maximum . the player record of this row is patrick ewing .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; jersey number ( s ) ; 2 } ; player } ; patrick ewing }, interpretation: the average of the haat record of all rows is 261 .
Output: no
Command: round_eq { avg { all_rows ; haat } ; 261 }, interpretation: the average of the haat record of all rows is 261 .
Output: yes
Command: round_eq { avg { all_rows ; haat } ; 261 }, interpretation: select the rows whose tyre record fuzzily matches to e . for the constructor records of these rows , most of them fuzzily match to ferrari .
Output: no
Command: most_eq { filter_eq { all_rows ; tyre ; e } ; constructor ; ferrari }, interpretation: select the rows whose tyre record fuzzily matches to e . for the constructor records of these rows , most of them fuzzily match to ferrari .
Output: yes
Command: most_eq { filter_eq { all_rows ; tyre ; e } ; constructor ; ferrari }, interpretation: select the row whose time record of all rows is 1st minimum . the winner record of this row is native heir .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; winner } ; native heir }, interpretation: select the row whose time record of all rows is 1st minimum . the winner record of this row is native heir .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; winner } ; native heir }, interpretation: select the rows whose date record fuzzily matches to december . there is only one such row in the table . the region record of this unqiue row is japan .
Output: no
Command: and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; region } ; japan } }, interpretation: select the rows whose date record fuzzily matches to december . there is only one such row in the table . the region record of this unqiue row is japan .
Output: yes
Command: and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; region } ; japan } }, interpretation: select the row whose seats ( acs ) record of all rows is 1st maximum . the state record of this row is madhya pradesh .
Output: no
Command: eq { hop { nth_argmax { all_rows ; seats ( acs ) ; 1 } ; state } ; madhya pradesh }, interpretation: select the row whose seats ( acs ) record of all rows is 1st maximum . the state record of this row is madhya pradesh .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; seats ( acs ) ; 1 } ; state } ; madhya pradesh }, interpretation: the average of the result record of all rows is 3rd place .
Output: no
Command: round_eq { avg { all_rows ; result } ; 3rd place }, interpretation: the average of the result record of all rows is 3rd place .
Output: yes
Command: round_eq { avg { all_rows ; result } ; 3rd place }, interpretation: the average of the attendance record of all rows is 16828.89 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 16828.89 }, interpretation: the average of the attendance record of all rows is 16828.89 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 16828.89 }, interpretation: select the rows whose political party record fuzzily matches to democratic party of albania . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; political party ; democratic party of albania } } ; 2 }, interpretation: select the rows whose political party record fuzzily matches to democratic party of albania . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; political party ; democratic party of albania } } ; 2 }, interpretation: for the engine records of all rows , all of them fuzzily match to ford v8 .
Output: no
Command: all_eq { all_rows ; engine ; ford v8 }, interpretation: for the engine records of all rows , all of them fuzzily match to ford v8 .
Output: yes
Command: all_eq { all_rows ; engine ; ford v8 }, interpretation: for the venue records of all rows , most of them fuzzily match to london .
Output: no
Command: most_eq { all_rows ; venue ; london }, interpretation: for the venue records of all rows , most of them fuzzily match to london .
Output: yes
Command: most_eq { all_rows ; venue ; london }, interpretation: select the rows whose rank record is arbitrary . the number of such rows is 9 .
Output: no
Command: eq { count { filter_all { all_rows ; rank } } ; 9 }, interpretation: select the rows whose rank record is arbitrary . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_all { all_rows ; rank } } ; 9 }, interpretation: select the row whose earnings record of all rows is maximum . the player record of this row is lee trevino .
Output: no
Command: eq { hop { argmax { all_rows ; earnings } ; player } ; lee trevino }, interpretation: select the row whose earnings record of all rows is maximum . the player record of this row is lee trevino .
Output: yes
Command: eq { hop { argmax { all_rows ; earnings } ; player } ; lee trevino }, interpretation: select the rows whose max memory record fuzzily matches to 128 gb . among these rows , select the rows whose max processors record fuzzily matches to 1 ultrasparc t2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; max memory ; 128 gb } ; max processors ; 1 ultrasparc t2 } } ; 2 }, interpretation: select the rows whose max memory record fuzzily matches to 128 gb . among these rows , select the rows whose max processors record fuzzily matches to 1 ultrasparc t2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; max memory ; 128 gb } ; max processors ; 1 ultrasparc t2 } } ; 2 }, interpretation: select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 13 .
Output: no
Command: eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 13 }, interpretation: select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 13 .
Output: yes
Command: eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 13 }, interpretation: for the race leader records of all rows , most of them fuzzily match to luigi marchisio ( ita ) .
Output: no
Command: most_eq { all_rows ; race leader ; luigi marchisio ( ita ) }, interpretation: for the race leader records of all rows , most of them fuzzily match to luigi marchisio ( ita ) .
Output: yes
Command: most_eq { all_rows ; race leader ; luigi marchisio ( ita ) }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is united states .
Output: no
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; united states }, interpretation: select the rows whose name record fuzzily matches to eclair . take the launched record of this row . select the rows whose name record fuzzily matches to derwent . take the launched record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; eclair } ; launched } ; hop { filter_eq { all_rows ; name ; derwent } ; launched } }, interpretation: select the rows whose name record fuzzily matches to eclair . take the launched record of this row . select the rows whose name record fuzzily matches to derwent . take the launched record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; eclair } ; launched } ; hop { filter_eq { all_rows ; name ; derwent } ; launched } }, interpretation: select the rows whose venue record fuzzily matches to trent bridge . there is only one such row in the table . the date record of this unqiue row is 23 , 24 , 25 , 26 , 27 july 1998 .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; trent bridge } } ; eq { hop { filter_eq { all_rows ; venue ; trent bridge } ; date } ; 23 , 24 , 25 , 26 , 27 july 1998 } }, interpretation: select the rows whose venue record fuzzily matches to trent bridge . there is only one such row in the table . the date record of this unqiue row is 23 , 24 , 25 , 26 , 27 july 1998 .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; trent bridge } } ; eq { hop { filter_eq { all_rows ; venue ; trent bridge } ; date } ; 23 , 24 , 25 , 26 , 27 july 1998 } }, interpretation: select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia } } ; 2 }, interpretation: select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia } } ; 2 }, interpretation: select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is the new normal . the directed by record of this row is michael lange .
Output: no
Command: and { eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; the new normal } ; eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; directed by } ; michael lange } }, interpretation: select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is the new normal . the directed by record of this row is michael lange .
Output: yes
Command: and { eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; the new normal } ; eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; directed by } ; michael lange } }, interpretation: for the date records of all rows , all of them fuzzily match to december .
Output: no
Command: all_eq { all_rows ; date ; december }, interpretation: select the rows whose driver / passenger record fuzzily matches to daniãl willemsen / sven verbrugge 1 . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to janis daiders / lauris daiders . take the points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders } ; points } }, interpretation: select the rows whose driver / passenger record fuzzily matches to daniãl willemsen / sven verbrugge 1 . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to janis daiders / lauris daiders . take the points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders } ; points } }, interpretation: select the row whose result record of all rows is 1st maximum . the incumbent record of this row is john lewis .
Output: no
Command: eq { hop { nth_argmax { all_rows ; result ; 1 } ; incumbent } ; john lewis }, interpretation: select the row whose result record of all rows is 1st maximum . the incumbent record of this row is john lewis .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; result ; 1 } ; incumbent } ; john lewis }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is october 7 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; october 7 }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is october 7 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; october 7 }, interpretation: the average of the laps record of all rows is 164 .
Output: no
Command: round_eq { avg { all_rows ; laps } ; 164 }, interpretation: the average of the laps record of all rows is 164 .
Output: yes
Command: round_eq { avg { all_rows ; laps } ; 164 }, interpretation: select the rows whose blank ends record is less than 10 . there is only one such row in the table . the skip record of this unqiue row is johnny frederiksen .
Output: no
Command: and { only { filter_less { all_rows ; blank ends ; 10 } } ; eq { hop { filter_less { all_rows ; blank ends ; 10 } ; skip } ; johnny frederiksen } }, interpretation: select the rows whose blank ends record is less than 10 . there is only one such row in the table . the skip record of this unqiue row is johnny frederiksen .
Output: yes
Command: and { only { filter_less { all_rows ; blank ends ; 10 } } ; eq { hop { filter_less { all_rows ; blank ends ; 10 } ; skip } ; johnny frederiksen } }, interpretation: select the rows whose result record fuzzily matches to loss . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; loss } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to loss . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; loss } } ; 3 }, interpretation: select the rows whose location record fuzzily matches to kohl center madison , wi . there is only one such row in the table . the acc team record of this unqiue row is nc state .
Output: no
Command: and { only { filter_eq { all_rows ; location ; kohl center madison , wi } } ; eq { hop { filter_eq { all_rows ; location ; kohl center madison , wi } ; acc team } ; nc state } }, interpretation: select the rows whose location record fuzzily matches to kohl center madison , wi . there is only one such row in the table . the acc team record of this unqiue row is nc state .
Output: yes
Command: and { only { filter_eq { all_rows ; location ; kohl center madison , wi } } ; eq { hop { filter_eq { all_rows ; location ; kohl center madison , wi } ; acc team } ; nc state } }, interpretation: select the row whose first elected record of all rows is 7th minimum . the district record of this row is south carolina 3 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 7 } ; district } ; south carolina 3 }, interpretation: select the row whose first elected record of all rows is 7th minimum . the district record of this row is south carolina 3 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 7 } ; district } ; south carolina 3 }, interpretation: the 1st minimum rank record of all rows is 1 . the nation record of the row with 1st minimum rank record is japan . the gold record of the row with 1st minimum rank record is 6 . the total record of the row with 1st minimum rank record is 12 .
Output: no
Command: and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; nation } ; japan } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 } } } }, interpretation: the 1st minimum rank record of all rows is 1 . the nation record of the row with 1st minimum rank record is japan . the gold record of the row with 1st minimum rank record is 6 . the total record of the row with 1st minimum rank record is 12 .
Output: yes
Command: and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; nation } ; japan } ; and { eq { hop { nth_argmin { all_rows ; rank ; 1 } ; gold } ; 6 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; total } ; 12 } } } }, interpretation: for the date records of all rows , all of them fuzzily match to december .
Output: no
Command: all_eq { all_rows ; date ; december }, interpretation: for the year records of all rows , most of them are equal to 1986 .
Output: no
Command: most_eq { all_rows ; year ; 1986 }, interpretation: for the year records of all rows , most of them are equal to 1986 .
Output: yes
Command: most_eq { all_rows ; year ; 1986 }, interpretation: select the rows whose lost record is equal to 14 . there is only one such row in the table . the club record of this unqiue row is burry port rfc .
Output: no
Command: and { only { filter_eq { all_rows ; lost ; 14 } } ; eq { hop { filter_eq { all_rows ; lost ; 14 } ; club } ; burry port rfc } }, interpretation: select the rows whose lost record is equal to 14 . there is only one such row in the table . the club record of this unqiue row is burry port rfc .
Output: yes
Command: and { only { filter_eq { all_rows ; lost ; 14 } } ; eq { hop { filter_eq { all_rows ; lost ; 14 } ; club } ; burry port rfc } }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is grove city college .
Output: no
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; grove city college }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is grove city college .
Output: yes
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; grove city college }, interpretation: select the rows whose nationality record fuzzily matches to slovakia . there is only one such row in the table . the distance record of this unqiue row is 500 m .
Output: no
Command: and { only { filter_eq { all_rows ; nationality ; slovakia } } ; eq { hop { filter_eq { all_rows ; nationality ; slovakia } ; distance } ; 500 m } }, interpretation: select the rows whose nationality record fuzzily matches to slovakia . there is only one such row in the table . the distance record of this unqiue row is 500 m .
Output: yes
Command: and { only { filter_eq { all_rows ; nationality ; slovakia } } ; eq { hop { filter_eq { all_rows ; nationality ; slovakia } ; distance } ; 500 m } }, interpretation: for the country records of all rows , all of them fuzzily match to united states .
Output: no
Command: all_eq { all_rows ; country ; united states }, interpretation: select the rows whose location record fuzzily matches to nevada , united states . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; location ; nevada , united states } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to nevada , united states . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location ; nevada , united states } } ; 2 }, interpretation: select the row whose score record of all rows is maximum . the team 1 record of this row is fc nantes ( d1 ) .
Output: no
Command: eq { hop { argmax { all_rows ; score } ; team 1 } ; fc nantes ( d1 ) }, interpretation: select the row whose score record of all rows is maximum . the team 1 record of this row is fc nantes ( d1 ) .
Output: yes
Command: eq { hop { argmax { all_rows ; score } ; team 1 } ; fc nantes ( d1 ) }, interpretation: select the row whose home team score record of all rows is 2nd maximum . the home team record of this row is essendon .
Output: no
Command: eq { hop { nth_argmax { all_rows ; home team score ; 2 } ; home team } ; essendon }, interpretation: select the row whose home team score record of all rows is 2nd maximum . the home team record of this row is essendon .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; home team score ; 2 } ; home team } ; essendon }, interpretation: select the row whose maximum seating capacity record of all rows is maximum . the arena / venue record of this row is la salle coliseum .
Output: no
Command: eq { hop { argmax { all_rows ; maximum seating capacity } ; arena / venue } ; la salle coliseum }, interpretation: select the row whose maximum seating capacity record of all rows is maximum . the arena / venue record of this row is la salle coliseum .
Output: yes
Command: eq { hop { argmax { all_rows ; maximum seating capacity } ; arena / venue } ; la salle coliseum }, interpretation: select the rows whose outgoing manager record fuzzily matches to raşit çetiner . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu . take the date of vacancy record of this row . the second record is 8 days larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy } } ; -8 days }, interpretation: select the rows whose outgoing manager record fuzzily matches to raşit çetiner . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu . take the date of vacancy record of this row . the second record is 8 days larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy } } ; -8 days }, interpretation: select the rows whose player record fuzzily matches to frederick l conklin . take the touchdowns record of this row . select the rows whose player record fuzzily matches to jimmy craig . take the touchdowns record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; frederick l conklin } ; touchdowns } ; hop { filter_eq { all_rows ; player ; jimmy craig } ; touchdowns } }, interpretation: select the rows whose player record fuzzily matches to frederick l conklin . take the touchdowns record of this row . select the rows whose player record fuzzily matches to jimmy craig . take the touchdowns record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; frederick l conklin } ; touchdowns } ; hop { filter_eq { all_rows ; player ; jimmy craig } ; touchdowns } }, interpretation: the average of the orangemen points record of all rows is 20.3 .
Output: no
Command: round_eq { avg { all_rows ; orangemen points } ; 20.3 }, interpretation: the average of the orangemen points record of all rows is 20.3 .
Output: yes
Command: round_eq { avg { all_rows ; orangemen points } ; 20.3 }, interpretation: select the rows whose position record fuzzily matches to defensive tackle . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; defensive tackle } } ; 2 }, interpretation: select the rows whose position record fuzzily matches to defensive tackle . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; defensive tackle } } ; 2 }, interpretation: select the row whose points record of all rows is maximum . the team record of this row is palestra itália - sp .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; team } ; palestra itália - sp }, interpretation: select the row whose points record of all rows is maximum . the team record of this row is palestra itália - sp .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; team } ; palestra itália - sp }, interpretation: for the surface records of all rows , most of them fuzzily match to dirt .
Output: no
Command: most_eq { all_rows ; surface ; dirt }, interpretation: for the surface records of all rows , most of them fuzzily match to dirt .
Output: yes
Command: most_eq { all_rows ; surface ; dirt }, interpretation: select the rows whose institution record fuzzily matches to minot state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dakota state university . take the enrollment record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; institution ; minot state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dakota state university } ; enrollment } }, interpretation: select the rows whose institution record fuzzily matches to minot state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dakota state university . take the enrollment record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; institution ; minot state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dakota state university } ; enrollment } }, interpretation: select the row whose us air date record of all rows is 3rd minimum . the us air date record of this row is july 3 , 1993 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; us air date ; 3 } ; us air date } ; july 3 , 1993 }, interpretation: select the row whose us air date record of all rows is 3rd minimum . the us air date record of this row is july 3 , 1993 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; us air date ; 3 } ; us air date } ; july 3 , 1993 }, interpretation: select the rows whose player record fuzzily matches to sura , bob bob sura . take the years for rockets record of this row . select the rows whose player record fuzzily matches to stroud , john john stroud . take the years for rockets record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; sura , bob bob sura } ; years for rockets } ; hop { filter_eq { all_rows ; player ; stroud , john john stroud } ; years for rockets } }, interpretation: select the rows whose player record fuzzily matches to sura , bob bob sura . take the years for rockets record of this row . select the rows whose player record fuzzily matches to stroud , john john stroud . take the years for rockets record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; sura , bob bob sura } ; years for rockets } ; hop { filter_eq { all_rows ; player ; stroud , john john stroud } ; years for rockets } }, interpretation: select the rows whose date record fuzzily matches to october . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; october } } ; 8 }, interpretation: select the rows whose date record fuzzily matches to october . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; october } } ; 8 }, interpretation: select the rows whose date of appointment record fuzzily matches to 3 november 2008 . there is only one such row in the table . the outgoing manager record of this unqiue row is osman özdemir .
Output: no
Command: and { only { filter_eq { all_rows ; date of appointment ; 3 november 2008 } } ; eq { hop { filter_eq { all_rows ; date of appointment ; 3 november 2008 } ; outgoing manager } ; osman özdemir } }, interpretation: select the rows whose date of appointment record fuzzily matches to 3 november 2008 . there is only one such row in the table . the outgoing manager record of this unqiue row is osman özdemir .
Output: yes
Command: and { only { filter_eq { all_rows ; date of appointment ; 3 november 2008 } } ; eq { hop { filter_eq { all_rows ; date of appointment ; 3 november 2008 } ; outgoing manager } ; osman özdemir } }, interpretation: for the year joined records of all rows , most of them are less than 1978 .
Output: no
Command: most_less { all_rows ; year joined ; 1978 }, interpretation: for the year joined records of all rows , most of them are less than 1978 .
Output: yes
Command: most_less { all_rows ; year joined ; 1978 }, interpretation: the 4th minimum week record of all rows is 4 . the attendance record of the row with 4th minimum week record is 55883 .
Output: no
Command: and { eq { nth_min { all_rows ; week ; 4 } ; 4 } ; eq { hop { nth_argmin { all_rows ; week ; 4 } ; attendance } ; 55883 } }, interpretation: the 4th minimum week record of all rows is 4 . the attendance record of the row with 4th minimum week record is 55883 .
Output: yes
Command: and { eq { nth_min { all_rows ; week ; 4 } ; 4 } ; eq { hop { nth_argmin { all_rows ; week ; 4 } ; attendance } ; 55883 } }, interpretation: select the rows whose nation record fuzzily matches to united kingdom . take the silver record of this row . select the rows whose nation record fuzzily matches to finland . take the silver record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nation ; united kingdom } ; silver } ; hop { filter_eq { all_rows ; nation ; finland } ; silver } }, interpretation: select the rows whose nation record fuzzily matches to united kingdom . take the silver record of this row . select the rows whose nation record fuzzily matches to finland . take the silver record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nation ; united kingdom } ; silver } ; hop { filter_eq { all_rows ; nation ; finland } ; silver } }, interpretation: select the rows whose joined record is equal to 2007 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; joined ; 2007 } } ; 2 }, interpretation: select the rows whose joined record is equal to 2007 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; joined ; 2007 } } ; 2 }, interpretation: the average of the area ( km square ) record of all rows is 198.448 .
Output: no
Command: round_eq { avg { all_rows ; area ( km square ) } ; 198.448 }, interpretation: the average of the area ( km square ) record of all rows is 198.448 .
Output: yes
Command: round_eq { avg { all_rows ; area ( km square ) } ; 198.448 }, interpretation: select the row whose completed record of all rows is 2nd maximum . the name record of this row is church of st thomas .
Output: no
Command: eq { hop { nth_argmax { all_rows ; completed ; 2 } ; name } ; church of st thomas }, interpretation: select the row whose completed record of all rows is 2nd maximum . the name record of this row is church of st thomas .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; completed ; 2 } ; name } ; church of st thomas }, interpretation: select the row whose championships in st louis record of all rows is maximum . the team record of this row is st louis stars .
Output: no
Command: eq { hop { argmax { all_rows ; championships in st louis } ; team } ; st louis stars }, interpretation: select the row whose championships in st louis record of all rows is maximum . the team record of this row is st louis stars .
Output: yes
Command: eq { hop { argmax { all_rows ; championships in st louis } ; team } ; st louis stars }, interpretation: select the rows whose county team record fuzzily matches to kilkenny . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; county team ; kilkenny } } ; 4 }, interpretation: select the rows whose county team record fuzzily matches to kilkenny . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; county team ; kilkenny } } ; 4 }, interpretation: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high rebounds record fuzzily matches to dwight howard . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high rebounds ; dwight howard } } ; 5 }, interpretation: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high rebounds record fuzzily matches to dwight howard . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high rebounds ; dwight howard } } ; 5 }, interpretation: select the rows whose away team score record fuzzily matches to 9.12 ( 66 ) . the maximum crowd record of these rows is 29374 .
Output: no
Command: eq { max { filter_eq { all_rows ; away team score ; 9.12 ( 66 ) } ; crowd } ; 29374 }, interpretation: select the rows whose away team score record fuzzily matches to 9.12 ( 66 ) . the maximum crowd record of these rows is 29374 .
Output: yes
Command: eq { max { filter_eq { all_rows ; away team score ; 9.12 ( 66 ) } ; crowd } ; 29374 }, interpretation: select the rows whose status record fuzzily matches to retired republican hold . there is only one such row in the table . the district record of this unqiue row is minnesota3 .
Output: no
Command: and { only { filter_eq { all_rows ; status ; retired republican hold } } ; eq { hop { filter_eq { all_rows ; status ; retired republican hold } ; district } ; minnesota3 } }, interpretation: select the rows whose status record fuzzily matches to retired republican hold . there is only one such row in the table . the district record of this unqiue row is minnesota3 .
Output: yes
Command: and { only { filter_eq { all_rows ; status ; retired republican hold } } ; eq { hop { filter_eq { all_rows ; status ; retired republican hold } ; district } ; minnesota3 } }, interpretation: select the rows whose cuts made record is less than 20 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { all_rows ; cuts made ; 20 } } ; 3 }, interpretation: select the rows whose cuts made record is less than 20 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { all_rows ; cuts made ; 20 } } ; 3 }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park }, interpretation: the 1st minimum place record of all rows is 1 . the player record of the row with 1st minimum place record is vijay singh .
Output: no
Command: and { eq { nth_min { all_rows ; place ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; place ; 1 } ; player } ; vijay singh } }, interpretation: the 1st minimum place record of all rows is 1 . the player record of the row with 1st minimum place record is vijay singh .
Output: yes
Command: and { eq { nth_min { all_rows ; place ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; place ; 1 } ; player } ; vijay singh } }, interpretation: for the country records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; country ; united states }, interpretation: select the rows whose opponent record fuzzily matches to chicago bears . take the date record of this row . select the rows whose opponent record fuzzily matches to staten island stapletons . take the date record of this row . the second record is 7 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; opponent ; chicago bears } ; date } ; hop { filter_eq { all_rows ; opponent ; staten island stapletons } ; date } } ; -7 }, interpretation: select the rows whose opponent record fuzzily matches to chicago bears . take the date record of this row . select the rows whose opponent record fuzzily matches to staten island stapletons . take the date record of this row . the second record is 7 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; opponent ; chicago bears } ; date } ; hop { filter_eq { all_rows ; opponent ; staten island stapletons } ; date } } ; -7 }, interpretation: select the row whose original air date record of all rows is 1st maximum . the title record of this row is weeping willows .
Output: no
Command: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; weeping willows }, interpretation: select the row whose original air date record of all rows is 1st maximum . the title record of this row is weeping willows .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; weeping willows }, interpretation: select the rows whose 2nd venue record fuzzily matches to mexico city . there is only one such row in the table . the year record of this unqiue row is 2007 .
Output: no
Command: and { only { filter_eq { all_rows ; 2nd venue ; mexico city } } ; eq { hop { filter_eq { all_rows ; 2nd venue ; mexico city } ; year } ; 2007 } }, interpretation: select the rows whose 2nd venue record fuzzily matches to mexico city . there is only one such row in the table . the year record of this unqiue row is 2007 .
Output: yes
Command: and { only { filter_eq { all_rows ; 2nd venue ; mexico city } } ; eq { hop { filter_eq { all_rows ; 2nd venue ; mexico city } ; year } ; 2007 } }, interpretation: select the rows whose position record fuzzily matches to defensive back . the minimum overall record of these rows is 22 .
Output: no
Command: eq { min { filter_eq { all_rows ; position ; defensive back } ; overall } ; 22 }, interpretation: select the rows whose position record fuzzily matches to defensive back . the minimum overall record of these rows is 22 .
Output: yes
Command: eq { min { filter_eq { all_rows ; position ; defensive back } ; overall } ; 22 }, interpretation: the average of the frequency ( per hour ) record of all rows is 3.11 .
Output: no
Command: round_eq { avg { all_rows ; frequency ( per hour ) } ; 3.11 }, interpretation: the average of the frequency ( per hour ) record of all rows is 3.11 .
Output: yes