niryuu/sni-each-converted huggingface dataset:
Command: most_eq { all_rows ; headquarters ; united states }, interpretation: select the rows whose wins record is greater than 4 . among these rows , select the rows whose loses record is equal to 2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; wins ; 4 } ; loses ; 2 } } ; 2 }, interpretation: select the rows whose wins record is greater than 4 . among these rows , select the rows whose loses record is equal to 2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; wins ; 4 } ; loses ; 2 } } ; 2 }, interpretation: the minimum place record of all rows is 1st . the song record of the row with superlative place record is what 's another year .
Output: no
Command: and { eq { min { all_rows ; place } ; 1st } ; eq { hop { argmin { all_rows ; place } ; song } ; what 's another year } }, interpretation: the minimum place record of all rows is 1st . the song record of the row with superlative place record is what 's another year .
Output: yes
Command: and { eq { min { all_rows ; place } ; 1st } ; eq { hop { argmin { all_rows ; place } ; song } ; what 's another year } }, interpretation: select the row whose date record of all rows is 2nd minimum . the score record of this row is 3 - 6 , 2 - 6 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 3 - 6 , 2 - 6 }, interpretation: select the row whose date record of all rows is 2nd minimum . the score record of this row is 3 - 6 , 2 - 6 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 3 - 6 , 2 - 6 }, interpretation: select the rows whose college record fuzzily matches to iowa state . there is only one such row in the table . the name record of this unqiue row is clyde shugart .
Output: no
Command: and { only { filter_eq { all_rows ; college ; iowa state } } ; eq { hop { filter_eq { all_rows ; college ; iowa state } ; name } ; clyde shugart } }, interpretation: select the rows whose college record fuzzily matches to iowa state . there is only one such row in the table . the name record of this unqiue row is clyde shugart .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; iowa state } } ; eq { hop { filter_eq { all_rows ; college ; iowa state } ; name } ; clyde shugart } }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is brunswick street oval .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; brunswick street oval }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is brunswick street oval .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; brunswick street oval }, interpretation: the 2nd minimum date record of all rows is jun 25 . the score record of the row with 2nd minimum date record is 2 - 3 .
Output: no
Command: and { eq { nth_min { all_rows ; date ; 2 } ; jun 25 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 2 - 3 } }, interpretation: the 2nd minimum date record of all rows is jun 25 . the score record of the row with 2nd minimum date record is 2 - 3 .
Output: yes
Command: and { eq { nth_min { all_rows ; date ; 2 } ; jun 25 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 2 - 3 } }, interpretation: the sum of the ends won record of all rows is 516 .
Output: no
Command: round_eq { sum { all_rows ; ends won } ; 516 }, interpretation: the sum of the ends won record of all rows is 516 .
Output: yes
Command: round_eq { sum { all_rows ; ends won } ; 516 }, interpretation: select the rows whose year record fuzzily matches to 1987 . take the points record of this row . select the rows whose year record fuzzily matches to 1995 . 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 ; year ; 1987 } ; points } ; hop { filter_eq { all_rows ; year ; 1995 } ; points } }, interpretation: select the rows whose year record fuzzily matches to 1987 . take the points record of this row . select the rows whose year record fuzzily matches to 1995 . 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 ; year ; 1987 } ; points } ; hop { filter_eq { all_rows ; year ; 1995 } ; points } }, interpretation: select the rows whose home team score record is greater than 10 . for the crowd records of these rows , most of them are less than 20000 .
Output: no
Command: most_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 }, interpretation: select the rows whose home team score record is greater than 10 . for the crowd records of these rows , most of them are less than 20000 .
Output: yes
Command: most_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 }, interpretation: select the rows whose res record fuzzily matches to win . among these rows , select the rows whose round record is equal to 1 . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; res ; win } ; round ; 1 } } ; 8 }, interpretation: select the rows whose res record fuzzily matches to win . among these rows , select the rows whose round record is equal to 1 . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; res ; win } ; round ; 1 } } ; 8 }, interpretation: select the rows whose try bonus record is equal to 1 . there is only one such row in the table . the club record of this unqiue row is cefn coed rfc .
Output: no
Command: and { only { filter_eq { all_rows ; try bonus ; 1 } } ; eq { hop { filter_eq { all_rows ; try bonus ; 1 } ; club } ; cefn coed rfc } }, interpretation: select the rows whose try bonus record is equal to 1 . there is only one such row in the table . the club record of this unqiue row is cefn coed rfc .
Output: yes
Command: and { only { filter_eq { all_rows ; try bonus ; 1 } } ; eq { hop { filter_eq { all_rows ; try bonus ; 1 } ; club } ; cefn coed rfc } }, interpretation: select the rows whose mult 1 record fuzzily matches to 8x . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; mult 1 ; 8x } } ; 2 }, interpretation: select the rows whose mult 1 record fuzzily matches to 8x . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; mult 1 ; 8x } } ; 2 }, interpretation: for the number of aircraft records of all rows , most of them are greater than 40 .
Output: no
Command: most_greater { all_rows ; number of aircraft ; 40 }, interpretation: for the number of aircraft records of all rows , most of them are greater than 40 .
Output: yes
Command: most_greater { all_rows ; number of aircraft ; 40 }, interpretation: select the rows whose result record fuzzily matches to lost re - election democratic gain . there is only one such row in the table . the incumbent record of this unqiue row is fred e busbey .
Output: no
Command: and { only { filter_eq { all_rows ; result ; lost re - election democratic gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent } ; fred e busbey } }, interpretation: select the rows whose result record fuzzily matches to lost re - election democratic gain . there is only one such row in the table . the incumbent record of this unqiue row is fred e busbey .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; lost re - election democratic gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent } ; fred e busbey } }, interpretation: select the rows whose country record fuzzily matches to northern ireland . there is only one such row in the table . the player record of this unqiue row is david feherty .
Output: no
Command: and { only { filter_eq { all_rows ; country ; northern ireland } } ; eq { hop { filter_eq { all_rows ; country ; northern ireland } ; player } ; david feherty } }, interpretation: select the rows whose country record fuzzily matches to northern ireland . there is only one such row in the table . the player record of this unqiue row is david feherty .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; northern ireland } } ; eq { hop { filter_eq { all_rows ; country ; northern ireland } ; player } ; david feherty } }, interpretation: the 1st minimum us release date record of all rows is 27 june 2006 . the title record of the row with 1st minimum us release date record is galaxy angel .
Output: no
Command: and { eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 } ; eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel } }, interpretation: the 1st minimum us release date record of all rows is 27 june 2006 . the title record of the row with 1st minimum us release date record is galaxy angel .
Output: yes
Command: and { eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 } ; eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel } }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is indiana university southeast .
Output: no
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; indiana university southeast }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is indiana university southeast .
Output: yes
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; indiana university southeast }, interpretation: select the rows whose regular season record is greater than 600 . there is only one such row in the table . the name record of this unqiue row is bobby hull .
Output: no
Command: and { only { filter_greater { all_rows ; regular season ; 600 } } ; eq { hop { filter_greater { all_rows ; regular season ; 600 } ; name } ; bobby hull } }, interpretation: select the rows whose regular season record is greater than 600 . there is only one such row in the table . the name record of this unqiue row is bobby hull .
Output: yes
Command: and { only { filter_greater { all_rows ; regular season ; 600 } } ; eq { hop { filter_greater { all_rows ; regular season ; 600 } ; name } ; bobby hull } }, interpretation: the maximum highest record of all rows is 1398 . the team record of the row with superlative highest record is dumbarton .
Output: no
Command: and { eq { max { all_rows ; highest } ; 1398 } ; eq { hop { argmax { all_rows ; highest } ; team } ; dumbarton } }, interpretation: the maximum highest record of all rows is 1398 . the team record of the row with superlative highest record is dumbarton .
Output: yes
Command: and { eq { max { all_rows ; highest } ; 1398 } ; eq { hop { argmax { all_rows ; highest } ; team } ; dumbarton } }, interpretation: the sum of the points record of all rows is 0 .
Output: no
Command: round_eq { sum { all_rows ; points } ; 0 }, interpretation: for the notes records of all rows , most of them fuzzily match to short film .
Output: no
Command: most_eq { all_rows ; notes ; short film }, interpretation: for the notes records of all rows , most of them fuzzily match to short film .
Output: yes
Command: most_eq { all_rows ; notes ; short film }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the visitor record of this row is kings .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; visitor } ; kings }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the visitor record of this row is kings .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; visitor } ; kings }, interpretation: for the livery records of all rows , most of them fuzzily match to pacific national blue & yellow .
Output: no
Command: most_eq { all_rows ; livery ; pacific national blue & yellow }, interpretation: for the livery records of all rows , most of them fuzzily match to pacific national blue & yellow .
Output: yes
Command: most_eq { all_rows ; livery ; pacific national blue & yellow }, interpretation: select the rows whose institution record fuzzily matches to university of texas at austin . take the enrollment record of this row . select the rows whose institution record fuzzily matches to texas christian 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 ; university of texas at austin } ; enrollment } ; hop { filter_eq { all_rows ; institution ; texas christian university } ; enrollment } }, interpretation: select the rows whose institution record fuzzily matches to university of texas at austin . take the enrollment record of this row . select the rows whose institution record fuzzily matches to texas christian 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 ; university of texas at austin } ; enrollment } ; hop { filter_eq { all_rows ; institution ; texas christian university } ; enrollment } }, interpretation: select the rows whose english title record fuzzily matches to bad education . take the year record of this row . select the rows whose english title record fuzzily matches to city of god . take the year record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; english title ; bad education } ; year } ; hop { filter_eq { all_rows ; english title ; city of god } ; year } }, interpretation: select the rows whose english title record fuzzily matches to bad education . take the year record of this row . select the rows whose english title record fuzzily matches to city of god . take the year record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; english title ; bad education } ; year } ; hop { filter_eq { all_rows ; english title ; city of god } ; year } }, interpretation: for the no of installments records of all rows , most of them are less than 100 .
Output: no
Command: most_less { all_rows ; no of installments ; 100 }, interpretation: for the no of installments records of all rows , most of them are less than 100 .
Output: yes
Command: most_less { all_rows ; no of installments ; 100 }, interpretation: the sum of the score record of all rows is 4577 .
Output: no
Command: round_eq { sum { all_rows ; score } ; 4577 }, interpretation: the sum of the score record of all rows is 4577 .
Output: yes
Command: round_eq { sum { all_rows ; score } ; 4577 }, interpretation: select the rows whose leading scorer record fuzzily matches to tony parker . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; leading scorer ; tony parker } } ; 4 }, interpretation: select the rows whose leading scorer record fuzzily matches to tony parker . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; leading scorer ; tony parker } } ; 4 }, interpretation: select the rows whose result record fuzzily matches to win . for the wildcats points records of these rows , most of them are greater than 20 .
Output: no
Command: most_greater { filter_eq { all_rows ; result ; win } ; wildcats points ; 20 }, interpretation: select the rows whose result record fuzzily matches to win . for the wildcats points records of these rows , most of them are greater than 20 .
Output: yes
Command: most_greater { filter_eq { all_rows ; result ; win } ; wildcats points ; 20 }, interpretation: select the rows whose county record fuzzily matches to oppland . there is only one such row in the table . the name record of this unqiue row is galdhøpiggen .
Output: no
Command: and { only { filter_eq { all_rows ; county ; oppland } } ; eq { hop { filter_eq { all_rows ; county ; oppland } ; name } ; galdhøpiggen } }, interpretation: select the rows whose county record fuzzily matches to oppland . there is only one such row in the table . the name record of this unqiue row is galdhøpiggen .
Output: yes
Command: and { only { filter_eq { all_rows ; county ; oppland } } ; eq { hop { filter_eq { all_rows ; county ; oppland } ; name } ; galdhøpiggen } }, interpretation: select the rows whose name record fuzzily matches to ray lindwall . take the wickets record of this row . select the rows whose name record fuzzily matches to clarrie grimmett . take the wickets record of this row . the first record is 12 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; ray lindwall } ; wickets } ; hop { filter_eq { all_rows ; name ; clarrie grimmett } ; wickets } } ; 12 }, interpretation: select the rows whose name record fuzzily matches to ray lindwall . take the wickets record of this row . select the rows whose name record fuzzily matches to clarrie grimmett . take the wickets record of this row . the first record is 12 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; ray lindwall } ; wickets } ; hop { filter_eq { all_rows ; name ; clarrie grimmett } ; wickets } } ; 12 }, interpretation: select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row . select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season } ; hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season } }, interpretation: select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row . select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season } ; hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season } }, interpretation: select the rows whose location attendance record fuzzily matches to united center . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; location attendance ; united center } } ; 3 }, interpretation: select the rows whose location attendance record fuzzily matches to united center . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location attendance ; united center } } ; 3 }, interpretation: the sum of the score record of all rows is 16 .
Output: no
Command: round_eq { sum { all_rows ; score } ; 16 }, interpretation: the sum of the score record of all rows is 16 .
Output: yes
Command: round_eq { sum { all_rows ; score } ; 16 }, interpretation: select the row whose gross record of all rows is 5th maximum . the title record of this row is the color purple . the director record of this row is steven spielberg .
Output: no
Command: and { eq { hop { nth_argmax { all_rows ; gross ; 5 } ; title } ; the color purple } ; eq { hop { nth_argmax { all_rows ; gross ; 5 } ; director } ; steven spielberg } }, interpretation: select the row whose gross record of all rows is 5th maximum . the title record of this row is the color purple . the director record of this row is steven spielberg .
Output: yes
Command: and { eq { hop { nth_argmax { all_rows ; gross ; 5 } ; title } ; the color purple } ; eq { hop { nth_argmax { all_rows ; gross ; 5 } ; director } ; steven spielberg } }, interpretation: select the row whose inn record of all rows is 2nd minimum . the team record of this row is arizona diamondbacks .
Output: no
Command: eq { hop { nth_argmin { all_rows ; inn ; 2 } ; team } ; arizona diamondbacks }, interpretation: select the row whose inn record of all rows is 2nd minimum . the team record of this row is arizona diamondbacks .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; inn ; 2 } ; team } ; arizona diamondbacks }, interpretation: select the rows whose score record is equal to 71 . the number of such rows is 11 .
Output: no
Command: eq { count { filter_eq { all_rows ; score ; 71 } } ; 11 }, interpretation: select the rows whose score record is equal to 71 . the number of such rows is 11 .
Output: yes
Command: eq { count { filter_eq { all_rows ; score ; 71 } } ; 11 }, interpretation: select the rows whose date record fuzzily matches to 12 june 1972 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; date ; 12 june 1972 } ; crowd ; 20000 } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 12 june 1972 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; date ; 12 june 1972 } ; crowd ; 20000 } } ; 2 }, interpretation: select the rows whose november record fuzzily matches to 27 . take the points record of this row . select the rows whose november record fuzzily matches to 29 . take the points record of this row . the second record is 2 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; november ; 27 } ; points } ; hop { filter_eq { all_rows ; november ; 29 } ; points } } ; -2 }, interpretation: select the rows whose november record fuzzily matches to 27 . take the points record of this row . select the rows whose november record fuzzily matches to 29 . take the points record of this row . the second record is 2 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; november ; 27 } ; points } ; hop { filter_eq { all_rows ; november ; 29 } ; points } } ; -2 }, interpretation: the maximum us viewers ( million ) record of all rows is 6.14 . the no in season record of the row with superlative us viewers ( million ) record is 1 .
Output: no
Command: and { eq { max { all_rows ; us viewers ( million ) } ; 6.14 } ; eq { hop { argmax { all_rows ; us viewers ( million ) } ; no in season } ; 1 } }, interpretation: the maximum us viewers ( million ) record of all rows is 6.14 . the no in season record of the row with superlative us viewers ( million ) record is 1 .
Output: yes
Command: and { eq { max { all_rows ; us viewers ( million ) } ; 6.14 } ; eq { hop { argmax { all_rows ; us viewers ( million ) } ; no in season } ; 1 } }, interpretation: select the rows whose gold record is equal to 6 . there is only one such row in the table . the nation record of this unqiue row is united states .
Output: no
Command: and { only { filter_eq { all_rows ; gold ; 6 } } ; eq { hop { filter_eq { all_rows ; gold ; 6 } ; nation } ; united states } }, interpretation: select the rows whose gold record is equal to 6 . there is only one such row in the table . the nation record of this unqiue row is united states .
Output: yes
Command: and { only { filter_eq { all_rows ; gold ; 6 } } ; eq { hop { filter_eq { all_rows ; gold ; 6 } ; nation } ; united states } }, interpretation: for the res records of all rows , most of them fuzzily match to win .
Output: no
Command: most_eq { all_rows ; res ; win }, interpretation: select the row whose facility id record of all rows is maximum . the call sign record of this row is wmhu .
Output: no
Command: eq { hop { argmax { all_rows ; facility id } ; call sign } ; wmhu }, interpretation: select the row whose facility id record of all rows is maximum . the call sign record of this row is wmhu .
Output: yes
Command: eq { hop { argmax { all_rows ; facility id } ; call sign } ; wmhu }, interpretation: select the row whose away team score record of all rows is maximum . the away team record of this row is south melbourne .
Output: no
Command: eq { hop { argmax { all_rows ; away team score } ; away team } ; south melbourne }, interpretation: select the row whose away team score record of all rows is maximum . the away team record of this row is south melbourne .
Output: yes
Command: eq { hop { argmax { all_rows ; away team score } ; away team } ; south melbourne }, interpretation: select the rows whose location record fuzzily matches to liberty . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; location ; liberty } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to liberty . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location ; liberty } } ; 2 }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is thomas j bliley , jr .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; thomas j bliley , jr }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is thomas j bliley , jr .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; thomas j bliley , jr }, interpretation: the sum of the crowd record of all rows is 84000 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 84000 }, interpretation: the sum of the crowd record of all rows is 84000 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 84000 }, interpretation: select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; windy hill } } ; eq { hop { filter_eq { all_rows ; venue ; windy hill } ; home team } ; essendon } }, interpretation: select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; windy hill } } ; eq { hop { filter_eq { all_rows ; venue ; windy hill } ; home team } ; essendon } }, interpretation: select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; fox } } ; 2 }, interpretation: select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; fox } } ; 2 }, interpretation: for the type records of all rows , all of them fuzzily match to sovereign .
Output: no
Command: all_eq { all_rows ; type ; sovereign }, interpretation: for the type records of all rows , all of them fuzzily match to sovereign .
Output: yes
Command: all_eq { all_rows ; type ; sovereign }, interpretation: select the rows whose position record is arbitrary . the number of such rows is 13 .
Output: no
Command: eq { count { filter_all { all_rows ; position } } ; 13 }, interpretation: select the rows whose position record is arbitrary . the number of such rows is 13 .
Output: yes
Command: eq { count { filter_all { all_rows ; position } } ; 13 }, interpretation: select the rows whose original air date record fuzzily matches to april . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; original air date ; april } } ; 2 }, interpretation: select the rows whose original air date record fuzzily matches to april . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; original air date ; april } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row . select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row . the first record is 2 larger than the second record . the high rebounds record of the first row is duncan ( 17 ) . the high rebounds record of the second row is duncan ( 15 ) .
Output: no
Command: and { eq { diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) } ; eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) } } }, interpretation: select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row . select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row . the first record is 2 larger than the second record . the high rebounds record of the first row is duncan ( 17 ) . the high rebounds record of the second row is duncan ( 15 ) .
Output: yes
Command: and { eq { diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) } ; eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) } } }, interpretation: select the rows whose official name record fuzzily matches to hampton . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to sussex corner . take the area km 2 record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; official name ; hampton } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; sussex corner } ; area km 2 } }, interpretation: select the rows whose official name record fuzzily matches to hampton . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to sussex corner . take the area km 2 record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; official name ; hampton } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; sussex corner } ; area km 2 } }, interpretation: select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record . the fcc info record of the first row is fcc . the fcc info record of the second row is fcc .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } } ; and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } } }, interpretation: select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record . the fcc info record of the first row is fcc . the fcc info record of the second row is fcc .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } } ; and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } } }, interpretation: for the country records of all rows , most of them fuzzily match to soviet union .
Output: no
Command: most_eq { all_rows ; country ; soviet union }, interpretation: for the country records of all rows , most of them fuzzily match to soviet union .
Output: yes
Command: most_eq { all_rows ; country ; soviet union }, interpretation: select the rows whose points record is greater than 10 . select the row whose lost record of these rows is 2nd maximum . the team record of this row is aa palmeiras .
Output: no
Command: eq { hop { nth_argmax { filter_greater { all_rows ; points ; 10 } ; lost ; 2 } ; team } ; aa palmeiras }, interpretation: select the rows whose points record is greater than 10 . select the row whose lost record of these rows is 2nd maximum . the team record of this row is aa palmeiras .
Output: yes
Command: eq { hop { nth_argmax { filter_greater { all_rows ; points ; 10 } ; lost ; 2 } ; team } ; aa palmeiras }, interpretation: select the rows whose team name record fuzzily matches to isport international . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; team name ; isport international } } ; 4 }, interpretation: select the rows whose team name record fuzzily matches to isport international . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; team name ; isport international } } ; 4 }, interpretation: for the county records of all rows , most of them fuzzily match to perry .
Output: no
Command: most_eq { all_rows ; county ; perry }, interpretation: for the county records of all rows , most of them fuzzily match to perry .
Output: yes
Command: most_eq { all_rows ; county ; perry }, interpretation: select the row whose date of commissioning record of all rows is maximum . the unit number record of this row is 8 .
Output: no
Command: eq { hop { argmax { all_rows ; date of commissioning } ; unit number } ; 8 }, interpretation: select the row whose date of commissioning record of all rows is maximum . the unit number record of this row is 8 .
Output: yes
Command: eq { hop { argmax { all_rows ; date of commissioning } ; unit number } ; 8 }, interpretation: select the rows whose college / junior / club team record fuzzily matches to calgary centennials . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; college / junior / club team ; calgary centennials } } ; 2 }, interpretation: select the rows whose college / junior / club team record fuzzily matches to calgary centennials . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; college / junior / club team ; calgary centennials } } ; 2 }, interpretation: select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % . there is only one such row in the table . the currency record of this unqiue row is yen . the country record of this unqiue row is japan .
Output: no
Command: and { only { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } } ; and { eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen } ; eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan } } }, interpretation: select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % . there is only one such row in the table . the currency record of this unqiue row is yen . the country record of this unqiue row is japan .
Output: yes
Command: and { only { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } } ; and { eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen } ; eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan } } }, interpretation: select the row whose super g record of all rows is maximum . the season record of this row is 1990 .
Output: no
Command: eq { hop { argmax { all_rows ; super g } ; season } ; 1990 }, interpretation: select the row whose super g record of all rows is maximum . the season record of this row is 1990 .
Output: yes
Command: eq { hop { argmax { all_rows ; super g } ; season } ; 1990 }, interpretation: for the event records of all rows , most of them fuzzily match to freestyle .
Output: no
Command: most_eq { all_rows ; event ; freestyle }, interpretation: for the event records of all rows , most of them fuzzily match to freestyle .
Output: yes
Command: most_eq { all_rows ; event ; freestyle }, interpretation: for the year records of all rows , all of them are greater than 2000 .
Output: no
Command: all_greater { all_rows ; year ; 2000 }, interpretation: for the year records of all rows , all of them are greater than 2000 .
Output: yes
Command: all_greater { all_rows ; year ; 2000 }, interpretation: select the rows whose party record fuzzily matches to democratic . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; party ; democratic } } ; 4 }, interpretation: select the rows whose party record fuzzily matches to democratic . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; party ; democratic } } ; 4 }, interpretation: select the rows whose 2006 - 10 record is equal to 0.29 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; 2006 - 10 ; 0.29 } } ; 3 }, interpretation: select the rows whose 2006 - 10 record is equal to 0.29 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 2006 - 10 ; 0.29 } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to drew . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; drew } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to drew . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; drew } } ; 2 }, interpretation: for the date records of all rows , all of them fuzzily match to september .
Output: no
Command: all_eq { all_rows ; date ; september }, interpretation: for the date records of all rows , all of them fuzzily match to september .
Output: yes
Command: all_eq { all_rows ; date ; september }, interpretation: select the rows whose county record fuzzily matches to clark . take the casinos record of this row . select the rows whose county record fuzzily matches to south lake tahoe . take the casinos record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; county ; clark } ; casinos } ; hop { filter_eq { all_rows ; county ; south lake tahoe } ; casinos } }, interpretation: select the rows whose county record fuzzily matches to clark . take the casinos record of this row . select the rows whose county record fuzzily matches to south lake tahoe . take the casinos record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; county ; clark } ; casinos } ; hop { filter_eq { all_rows ; county ; south lake tahoe } ; casinos } }, interpretation: the sum of the races record of all rows is 190 .
Output: no
Command: round_eq { sum { all_rows ; races } ; 190 }, interpretation: the sum of the races record of all rows is 190 .
Output: yes
Command: round_eq { sum { all_rows ; races } ; 190 }, interpretation: for the 4th place records of all rows , all of them are equal to 0 .
Output: no
Command: all_eq { all_rows ; 4th place ; 0 }, interpretation: for the 4th place records of all rows , all of them are equal to 0 .
Output: yes
Command: all_eq { all_rows ; 4th place ; 0 }, interpretation: select the rows whose place ( result ) record fuzzily matches to 1st runner - up . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } } ; 2 }, interpretation: select the rows whose place ( result ) record fuzzily matches to 1st runner - up . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } } ; 2 }, interpretation: select the row whose shot % record of all rows is minimum . the skip record of this row is anna kubešková .
Output: no
Command: eq { hop { argmin { all_rows ; shot % } ; skip } ; anna kubešková }, interpretation: select the row whose shot % record of all rows is minimum . the skip record of this row is anna kubešková .
Output: yes
Command: eq { hop { argmin { all_rows ; shot % } ; skip } ; anna kubešková }, interpretation: select the row whose employed record of all rows is 2nd minimum . the name record of this row is zimmerman , roy alfred .
Output: no
Command: eq { hop { nth_argmin { all_rows ; employed ; 2 } ; name } ; zimmerman , roy alfred }, interpretation: select the row whose employed record of all rows is 2nd minimum . the name record of this row is zimmerman , roy alfred .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; employed ; 2 } ; name } ; zimmerman , roy alfred }, interpretation: select the rows whose lec sport record fuzzily matches to tennis . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; lec sport ; tennis } } ; 2 }, interpretation: select the rows whose lec sport record fuzzily matches to tennis . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; lec sport ; tennis } } ; 2 }, interpretation: select the row whose time record of all rows is 2nd maximum . the country record of this row is slovenia .
Output: no
Command: eq { hop { nth_argmax { all_rows ; time ; 2 } ; country } ; slovenia }, interpretation: select the row whose time record of all rows is 2nd maximum . the country record of this row is slovenia .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; time ; 2 } ; country } ; slovenia }, interpretation: select the rows whose opponents record fuzzily matches to anne hobbs andrew castle . take the year record of this row . select the rows whose opponents record fuzzily matches to gretchen magers kelly jones . take the year record of this row . the second record is 1 year larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; opponents ; anne hobbs andrew castle } ; year } ; hop { filter_eq { all_rows ; opponents ; gretchen magers kelly jones } ; year } } ; -1 year }, interpretation: select the rows whose opponents record fuzzily matches to anne hobbs andrew castle . take the year record of this row . select the rows whose opponents record fuzzily matches to gretchen magers kelly jones . take the year record of this row . the second record is 1 year larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; opponents ; anne hobbs andrew castle } ; year } ; hop { filter_eq { all_rows ; opponents ; gretchen magers kelly jones } ; year } } ; -1 year }, interpretation: the sum of the gold record of all rows is 3 .
Output: no
Command: round_eq { sum { all_rows ; gold } ; 3 }, interpretation: the sum of the gold record of all rows is 3 .
Output: yes
Command: round_eq { sum { all_rows ; gold } ; 3 }, interpretation: for the played records of all rows , all of them are equal to 16 .
Output: no
Command: all_eq { all_rows ; played ; 16 }, interpretation: for the played records of all rows , all of them are equal to 16 .
Output: yes
Command: all_eq { all_rows ; played ; 16 }, interpretation: for the original air date records of all rows , most of them fuzzily match to july .
Output: no
Command: most_eq { all_rows ; original air date ; july }, interpretation: for the original air date records of all rows , most of them fuzzily match to july .
Output: yes
Command: most_eq { all_rows ; original air date ; july }, interpretation: select the rows whose venue record fuzzily matches to mcg . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is collingwood .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; mcg } } ; and { eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond } ; eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood } } }, interpretation: select the rows whose venue record fuzzily matches to mcg . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is collingwood .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; mcg } } ; and { eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond } ; eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood } } }, interpretation: select the rows whose contestant record is arbitrary . the number of such rows is 19 .
Output: no
Command: eq { count { filter_all { all_rows ; contestant } } ; 19 }, interpretation: select the rows whose contestant record is arbitrary . the number of such rows is 19 .
Output: yes
Command: eq { count { filter_all { all_rows ; contestant } } ; 19 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is 1970 - 10 - 04 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; 1970 - 10 - 04 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is 1970 - 10 - 04 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; 1970 - 10 - 04 }, interpretation: select the rows whose silver record is equal to 3 . there is only one such row in the table . the nation record of this unqiue row is soviet union .
Output: no
Command: and { only { filter_eq { all_rows ; silver ; 3 } } ; eq { hop { filter_eq { all_rows ; silver ; 3 } ; nation } ; soviet union } }, interpretation: select the rows whose silver record is equal to 3 . there is only one such row in the table . the nation record of this unqiue row is soviet union .
Output: yes
Command: and { only { filter_eq { all_rows ; silver ; 3 } } ; eq { hop { filter_eq { all_rows ; silver ; 3 } ; nation } ; soviet union } }, interpretation: select the rows whose date record fuzzily matches to 1948 . the sum of the round record of these rows is 4 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; date ; 1948 } ; round } ; 4 }, interpretation: select the rows whose date record fuzzily matches to 1948 . the sum of the round record of these rows is 4 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; date ; 1948 } ; round } ; 4 }, interpretation: the sum of the gold record of all rows is 8 .
Output: no
Command: round_eq { sum { all_rows ; gold } ; 8 }, interpretation: the sum of the gold record of all rows is 8 .
Output: yes
Command: round_eq { sum { all_rows ; gold } ; 8 }, interpretation: select the rows whose gross record is greater than 300000000 . there is only one such row in the table . the title record of this unqiue row is fatal attraction .
Output: no
Command: and { only { filter_greater { all_rows ; gross ; 300000000 } } ; eq { hop { filter_greater { all_rows ; gross ; 300000000 } ; title } ; fatal attraction } }, interpretation: select the rows whose gross record is greater than 300000000 . there is only one such row in the table . the title record of this unqiue row is fatal attraction .
Output: yes
Command: and { only { filter_greater { all_rows ; gross ; 300000000 } } ; eq { hop { filter_greater { all_rows ; gross ; 300000000 } ; title } ; fatal attraction } }, interpretation: for the district records of all rows , most of them fuzzily match to hisar .
Output: no
Command: most_eq { all_rows ; district ; hisar }, interpretation: for the district records of all rows , most of them fuzzily match to hisar .
Output: yes
Command: most_eq { all_rows ; district ; hisar }, interpretation: the average of the score record of all rows is 55 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 55 }, interpretation: the average of the score record of all rows is 55 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 55 }, interpretation: the sum of the wins record of all rows is 18 .
Output: no
Command: round_eq { sum { all_rows ; wins } ; 18 }, interpretation: the sum of the wins record of all rows is 18 .
Output: yes
Command: round_eq { sum { all_rows ; wins } ; 18 }, interpretation: for the dominant religion ( 2002 ) records of all rows , all of them fuzzily match to orthodox christianity .
Output: no