niryuu/sni-each-converted huggingface dataset:
Command: and { only { filter_eq { all_rows ; date ; february } } ; eq { hop { filter_eq { all_rows ; date ; february } ; event } ; indianapolis boat , sport & travel show } }, interpretation: select the rows whose date record fuzzily matches to february . there is only one such row in the table . the event record of this unqiue row is indianapolis boat , sport & travel show .
Output: yes
Command: and { only { filter_eq { all_rows ; date ; february } } ; eq { hop { filter_eq { all_rows ; date ; february } ; event } ; indianapolis boat , sport & travel show } }, interpretation: for the episodes records of all rows , most of them are equal to 11 .
Output: no
Command: most_eq { all_rows ; episodes ; 11 }, interpretation: for the episodes records of all rows , most of them are equal to 11 .
Output: yes
Command: most_eq { all_rows ; episodes ; 11 }, interpretation: select the row whose average record of all rows is 2nd maximum . the team record of this row is river plate .
Output: no
Command: eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; river plate }, interpretation: select the row whose average record of all rows is 2nd maximum . the team record of this row is river plate .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; river plate }, interpretation: select the rows whose time / retired record fuzzily matches to clutch . for the laps records of these rows , most of them are greater than 30 .
Output: no
Command: most_greater { filter_eq { all_rows ; time / retired ; clutch } ; laps ; 30 }, interpretation: select the rows whose time / retired record fuzzily matches to clutch . for the laps records of these rows , most of them are greater than 30 .
Output: yes
Command: most_greater { filter_eq { all_rows ; time / retired ; clutch } ; laps ; 30 }, interpretation: select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } } }, interpretation: select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } } }, interpretation: select the rows whose october 20 , 2008 record fuzzily matches to 2010 . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; october 20 , 2008 ; 2010 } } ; 6 }, interpretation: select the rows whose october 20 , 2008 record fuzzily matches to 2010 . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; october 20 , 2008 ; 2010 } } ; 6 }, interpretation: for the high assists records of all rows , most of them fuzzily match to imari sawyer .
Output: no
Command: most_eq { all_rows ; high assists ; imari sawyer }, interpretation: for the high assists records of all rows , most of them fuzzily match to imari sawyer .
Output: yes
Command: most_eq { all_rows ; high assists ; imari sawyer }, interpretation: for the result records of all rows , most of them fuzzily match to 1st .
Output: no
Command: most_eq { all_rows ; result ; 1st }, interpretation: for the result records of all rows , most of them fuzzily match to 1st .
Output: yes
Command: most_eq { all_rows ; result ; 1st }, interpretation: select the row whose weight ( lbs ) record of all rows is maximum . the name record of this row is joseph barksdale .
Output: no
Command: eq { hop { argmax { all_rows ; weight ( lbs ) } ; name } ; joseph barksdale }, interpretation: select the row whose weight ( lbs ) record of all rows is maximum . the name record of this row is joseph barksdale .
Output: yes
Command: eq { hop { argmax { all_rows ; weight ( lbs ) } ; name } ; joseph barksdale }, interpretation: for the manner of departure records of all rows , most of them fuzzily match to sacked .
Output: no
Command: most_eq { all_rows ; manner of departure ; sacked }, interpretation: for the manner of departure records of all rows , most of them fuzzily match to sacked .
Output: yes
Command: most_eq { all_rows ; manner of departure ; sacked }, interpretation: select the rows whose opposition record fuzzily matches to pune warriors india . take the win % record of this row . select the rows whose opposition record fuzzily matches to kochi tuskers kerala . take the win % record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opposition ; pune warriors india } ; win % } ; hop { filter_eq { all_rows ; opposition ; kochi tuskers kerala } ; win % } }, interpretation: select the rows whose opposition record fuzzily matches to pune warriors india . take the win % record of this row . select the rows whose opposition record fuzzily matches to kochi tuskers kerala . take the win % record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opposition ; pune warriors india } ; win % } ; hop { filter_eq { all_rows ; opposition ; kochi tuskers kerala } ; win % } }, interpretation: select the rows whose silver record is greater than 0 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_greater { all_rows ; silver ; 0 } } ; 5 }, interpretation: select the rows whose silver record is greater than 0 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_greater { all_rows ; silver ; 0 } } ; 5 }, interpretation: select the rows whose home team score record is greater than or equal to 11 . for the crowd records of these rows , most of them are greater than 10000 .
Output: no
Command: most_greater { filter_greater_eq { all_rows ; home team score ; 11 } ; crowd ; 10000 }, interpretation: select the rows whose home team score record is greater than or equal to 11 . for the crowd records of these rows , most of them are greater than 10000 .
Output: yes
Command: most_greater { filter_greater_eq { all_rows ; home team score ; 11 } ; crowd ; 10000 }, interpretation: the sum of the population record of all rows is 22,500 .
Output: no
Command: round_eq { sum { all_rows ; population } ; 22,500 }, interpretation: the sum of the population record of all rows is 22,500 .
Output: yes
Command: round_eq { sum { all_rows ; population } ; 22,500 }, interpretation: select the rows whose mens singles record fuzzily matches to chen hong . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; mens singles ; chen hong } } ; 2 }, interpretation: select the rows whose mens singles record fuzzily matches to chen hong . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; mens singles ; chen hong } } ; 2 }, interpretation: select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is rijeka a , b .
Output: no
Command: eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; rijeka a , b }, interpretation: select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is rijeka a , b .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; rijeka a , b }, interpretation: select the row whose candidates record of all rows is maximum . the district record of this row is georgia 1 .
Output: no
Command: eq { hop { argmax { all_rows ; candidates } ; district } ; georgia 1 }, interpretation: select the row whose candidates record of all rows is maximum . the district record of this row is georgia 1 .
Output: yes
Command: eq { hop { argmax { all_rows ; candidates } ; district } ; georgia 1 }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is st kilda .
Output: no
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; st kilda }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is st kilda .
Output: yes
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; st kilda }, interpretation: the average of the number of deputies record of all rows is 114 .
Output: no
Command: round_eq { avg { all_rows ; number of deputies } ; 114 }, interpretation: the average of the number of deputies record of all rows is 114 .
Output: yes
Command: round_eq { avg { all_rows ; number of deputies } ; 114 }, interpretation: select the rows whose country record fuzzily matches to fra . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; fra } } ; 3 }, interpretation: select the rows whose country record fuzzily matches to fra . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; fra } } ; 3 }, interpretation: select the row whose attendance record of all rows is maximum . the week record of this row is 5 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; week } ; 5 }, interpretation: select the row whose attendance record of all rows is maximum . the week record of this row is 5 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; week } ; 5 }, interpretation: select the row whose top 10 record of all rows is maximum . the year record of this row is 2009 .
Output: no
Command: eq { hop { argmax { all_rows ; top 10 } ; year } ; 2009 }, interpretation: select the row whose top 10 record of all rows is maximum . the year record of this row is 2009 .
Output: yes
Command: eq { hop { argmax { all_rows ; top 10 } ; year } ; 2009 }, interpretation: the average of the attendance record of all rows is 16325 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 16325 }, interpretation: the average of the attendance record of all rows is 16325 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 16325 }, interpretation: select the rows whose screens record is greater than 1000 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_greater { all_rows ; screens ; 1000 } } ; 5 }, interpretation: select the rows whose screens record is greater than 1000 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_greater { all_rows ; screens ; 1000 } } ; 5 }, interpretation: select the rows whose nation record fuzzily matches to south korea . take the bronze record of this row . select the rows whose nation record fuzzily matches to estonia . take the bronze record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nation ; south korea } ; bronze } ; hop { filter_eq { all_rows ; nation ; estonia } ; bronze } }, interpretation: select the rows whose nation record fuzzily matches to south korea . take the bronze record of this row . select the rows whose nation record fuzzily matches to estonia . take the bronze record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nation ; south korea } ; bronze } ; hop { filter_eq { all_rows ; nation ; estonia } ; bronze } }, interpretation: the sum of the attendance record of all rows is 779652 .
Output: no
Command: round_eq { sum { all_rows ; attendance } ; 779652 }, interpretation: the sum of the attendance record of all rows is 779652 .
Output: yes
Command: round_eq { sum { all_rows ; attendance } ; 779652 }, interpretation: the average of the vertical record of all rows is 720 .
Output: no
Command: round_eq { avg { all_rows ; vertical } ; 720 }, interpretation: the average of the vertical record of all rows is 720 .
Output: yes
Command: round_eq { avg { all_rows ; vertical } ; 720 }, interpretation: select the rows whose rr romaja record fuzzily matches to gangwon . take the area record of this row . select the rows whose rr romaja record fuzzily matches to jeju . take the area record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; rr romaja ; gangwon } ; area } ; hop { filter_eq { all_rows ; rr romaja ; jeju } ; area } }, interpretation: select the rows whose rr romaja record fuzzily matches to gangwon . take the area record of this row . select the rows whose rr romaja record fuzzily matches to jeju . take the area record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; rr romaja ; gangwon } ; area } ; hop { filter_eq { all_rows ; rr romaja ; jeju } ; area } }, interpretation: select the rows whose surface record fuzzily matches to clay . the sum of the score record of these rows is 7 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; surface ; clay } ; score } ; 7 }, interpretation: select the rows whose surface record fuzzily matches to clay . the sum of the score record of these rows is 7 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; surface ; clay } ; score } ; 7 }, interpretation: select the row whose size record of all rows is 2nd maximum . the campus record of this row is kendall campus .
Output: no
Command: eq { hop { nth_argmax { all_rows ; size ; 2 } ; campus } ; kendall campus }, interpretation: select the row whose size record of all rows is 2nd maximum . the campus record of this row is kendall campus .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; size ; 2 } ; campus } ; kendall campus }, interpretation: select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; party ; democratic } } ; 5 }, interpretation: select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; party ; democratic } } ; 5 }, interpretation: the maximum money raised , 3q record of all rows is 11624255 .
Output: no
Command: eq { max { all_rows ; money raised , 3q } ; 11624255 }, interpretation: the maximum money raised , 3q record of all rows is 11624255 .
Output: yes
Command: eq { max { all_rows ; money raised , 3q } ; 11624255 }, interpretation: for the team records of all rows , most of them fuzzily match to rothmans honda .
Output: no
Command: most_eq { all_rows ; team ; rothmans honda }, interpretation: for the team records of all rows , most of them fuzzily match to rothmans honda .
Output: yes
Command: most_eq { all_rows ; team ; rothmans honda }, interpretation: select the row whose date record of all rows is maximum . the number & name record of this row is no 07005 .
Output: no
Command: eq { hop { argmax { all_rows ; date } ; number & name } ; no 07005 }, interpretation: select the row whose date record of all rows is maximum . the number & name record of this row is no 07005 .
Output: yes
Command: eq { hop { argmax { all_rows ; date } ; number & name } ; no 07005 }, interpretation: select the row whose time record of all rows is minimum . the nationality record of this row is australia .
Output: no
Command: eq { hop { argmin { all_rows ; time } ; nationality } ; australia }, interpretation: select the row whose time record of all rows is minimum . the nationality record of this row is australia .
Output: yes
Command: eq { hop { argmin { all_rows ; time } ; nationality } ; australia }, interpretation: for the tournament records of all rows , most of them fuzzily match to 25000 .
Output: no
Command: most_eq { all_rows ; tournament ; 25000 }, interpretation: for the tournament records of all rows , most of them fuzzily match to 25000 .
Output: yes
Command: most_eq { all_rows ; tournament ; 25000 }, interpretation: select the rows whose test record fuzzily matches to math . among these rows , select the rows whose number of students record is greater than 100000 . there is only one such row in the table .
Output: no
Command: only { filter_greater { filter_eq { all_rows ; test ; math } ; number of students ; 100000 } }, interpretation: select the rows whose test record fuzzily matches to math . among these rows , select the rows whose number of students record is greater than 100000 . there is only one such row in the table .
Output: yes
Command: only { filter_greater { filter_eq { all_rows ; test ; math } ; number of students ; 100000 } }, interpretation: select the rows whose division record fuzzily matches to southeast . the average of the pts record of these rows is 75.5 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; division ; southeast } ; pts } ; 75.5 }, interpretation: select the rows whose division record fuzzily matches to southeast . the average of the pts record of these rows is 75.5 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; division ; southeast } ; pts } ; 75.5 }, interpretation: select the rows whose school record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; school } } ; 6 }, interpretation: select the rows whose school record is arbitrary . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_all { all_rows ; school } } ; 6 }, interpretation: select the rows whose format record fuzzily matches to cd . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; format ; cd } } ; 7 }, interpretation: select the rows whose format record fuzzily matches to cd . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; format ; cd } } ; 7 }, interpretation: select the row whose average record of all rows is 2nd maximum . the team record of this row is boca juniors .
Output: no
Command: eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; boca juniors }, interpretation: select the row whose average record of all rows is 2nd maximum . the team record of this row is boca juniors .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; boca juniors }, interpretation: select the rows whose date record fuzzily matches to may 2008 . for the original artist records of these rows , all of them fuzzily match to beatles .
Output: no
Command: all_eq { filter_eq { all_rows ; date ; may 2008 } ; original artist ; beatles }, interpretation: select the rows whose date record fuzzily matches to may 2008 . for the original artist records of these rows , all of them fuzzily match to beatles .
Output: yes
Command: all_eq { filter_eq { all_rows ; date ; may 2008 } ; original artist ; beatles }, interpretation: select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the first record is less than the second record . the finish position record of the first row is 31st . the finish position record of the second row is 33rd .
Output: no
Command: and { less { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } } ; and { eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd } } }, interpretation: select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the first record is less than the second record . the finish position record of the first row is 31st . the finish position record of the second row is 33rd .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } } ; and { eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd } } }, interpretation: select the rows whose record label record fuzzily matches to wild world . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; record label ; wild world } } ; 3 }, interpretation: select the rows whose record label record fuzzily matches to wild world . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; record label ; wild world } } ; 3 }, interpretation: the average of the total record of all rows is 15.419 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 15.419 }, interpretation: the average of the total record of all rows is 15.419 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 15.419 }, interpretation: select the rows whose role record fuzzily matches to police officer lung . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; role ; police officer lung } } ; 5 }, interpretation: select the rows whose role record fuzzily matches to police officer lung . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; role ; police officer lung } } ; 5 }, interpretation: select the rows whose wins record is equal to 18 . there is only one such row in the table . the driver record of this unqiue row is daniël willemsen .
Output: no
Command: and { only { filter_eq { all_rows ; wins ; 18 } } ; eq { hop { filter_eq { all_rows ; wins ; 18 } ; driver } ; daniël willemsen } }, interpretation: select the rows whose wins record is equal to 18 . there is only one such row in the table . the driver record of this unqiue row is daniël willemsen .
Output: yes
Command: and { only { filter_eq { all_rows ; wins ; 18 } } ; eq { hop { filter_eq { all_rows ; wins ; 18 } ; driver } ; daniël willemsen } }, interpretation: for the surface records of all rows , all of them fuzzily match to clay .
Output: no
Command: all_eq { all_rows ; surface ; clay }, interpretation: for the suites records of all rows , most of them are greater than or equal to 125 .
Output: no
Command: most_greater_eq { all_rows ; suites ; 125 }, interpretation: for the suites records of all rows , most of them are greater than or equal to 125 .
Output: yes
Command: most_greater_eq { all_rows ; suites ; 125 }, interpretation: select the rows whose silver record is equal to 6 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; silver ; 6 } } ; 2 }, interpretation: select the rows whose silver record is equal to 6 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; silver ; 6 } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to l . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; l } } ; 8 }, interpretation: select the rows whose result record fuzzily matches to l . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; l } } ; 8 }, interpretation: the average of the crowd record of all rows is 20188 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 20188 }, interpretation: the average of the crowd record of all rows is 20188 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 20188 }, interpretation: select the rows whose english title record fuzzily matches to lives of omission . take the peak record of this row . select the rows whose english title record fuzzily matches to the rippling blossom . take the peak record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; english title ; lives of omission } ; peak } ; hop { filter_eq { all_rows ; english title ; the rippling blossom } ; peak } }, interpretation: select the rows whose english title record fuzzily matches to lives of omission . take the peak record of this row . select the rows whose english title record fuzzily matches to the rippling blossom . take the peak record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; english title ; lives of omission } ; peak } ; hop { filter_eq { all_rows ; english title ; the rippling blossom } ; peak } }, interpretation: select the rows whose population ( 2010 census ) record is less than 5000 . there is only one such row in the table . the district record of this unqiue row is intramuros .
Output: no
Command: and { only { filter_less { all_rows ; population ( 2010 census ) ; 5000 } } ; eq { hop { filter_less { all_rows ; population ( 2010 census ) ; 5000 } ; district } ; intramuros } }, interpretation: select the rows whose population ( 2010 census ) record is less than 5000 . there is only one such row in the table . the district record of this unqiue row is intramuros .
Output: yes
Command: and { only { filter_less { all_rows ; population ( 2010 census ) ; 5000 } } ; eq { hop { filter_less { all_rows ; population ( 2010 census ) ; 5000 } ; district } ; intramuros } }, interpretation: select the rows whose year record fuzzily matches to 2000 . take the goals scored record of this row . select the rows whose year record fuzzily matches to 1999 . take the goals scored record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 2000 } ; goals scored } ; hop { filter_eq { all_rows ; year ; 1999 } ; goals scored } }, interpretation: select the rows whose year record fuzzily matches to 2000 . take the goals scored record of this row . select the rows whose year record fuzzily matches to 1999 . take the goals scored record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 2000 } ; goals scored } ; hop { filter_eq { all_rows ; year ; 1999 } ; goals scored } }, interpretation: select the rows whose object type record fuzzily matches to spiral galaxy . among these rows , select the rows whose apparent magnitude record is equal to 13.0 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; object type ; spiral galaxy } ; apparent magnitude ; 13.0 } } ; 2 }, interpretation: select the rows whose object type record fuzzily matches to spiral galaxy . among these rows , select the rows whose apparent magnitude record is equal to 13.0 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; object type ; spiral galaxy } ; apparent magnitude ; 13.0 } } ; 2 }, interpretation: select the rows whose product record fuzzily matches to grain . take the 2001 record of this row . select the rows whose product record fuzzily matches to timber . take the 2001 record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; product ; grain } ; 2001 } ; hop { filter_eq { all_rows ; product ; timber } ; 2001 } }, interpretation: select the rows whose product record fuzzily matches to grain . take the 2001 record of this row . select the rows whose product record fuzzily matches to timber . take the 2001 record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; product ; grain } ; 2001 } ; hop { filter_eq { all_rows ; product ; timber } ; 2001 } }, interpretation: for the laps led records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; laps led ; 0 }, interpretation: for the laps led records of all rows , most of them are equal to 0 .
Output: yes
Command: most_eq { all_rows ; laps led ; 0 }, interpretation: for the final records of all rows , most of them fuzzily match to did not advance .
Output: no
Command: most_eq { all_rows ; final ; did not advance }, interpretation: for the final records of all rows , most of them fuzzily match to did not advance .
Output: yes
Command: most_eq { all_rows ; final ; did not advance }, interpretation: select the rows whose club record fuzzily matches to real murcia . take the wins record of this row . select the rows whose club record fuzzily matches to jerez cd . take the wins record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; club ; real murcia } ; wins } ; hop { filter_eq { all_rows ; club ; jerez cd } ; wins } }, interpretation: select the rows whose club record fuzzily matches to real murcia . take the wins record of this row . select the rows whose club record fuzzily matches to jerez cd . take the wins record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; club ; real murcia } ; wins } ; hop { filter_eq { all_rows ; club ; jerez cd } ; wins } }, interpretation: the average of the population served record of all rows is 4038 .
Output: no
Command: round_eq { avg { all_rows ; population served } ; 4038 }, interpretation: the average of the population served record of all rows is 4038 .
Output: yes
Command: round_eq { avg { all_rows ; population served } ; 4038 }, interpretation: the minimum closed record of all rows is 1983 . the station record of the row with superlative closed record is fairmount avenue .
Output: no
Command: and { eq { min { all_rows ; closed } ; 1983 } ; eq { hop { argmin { all_rows ; closed } ; station } ; fairmount avenue } }, interpretation: the minimum closed record of all rows is 1983 . the station record of the row with superlative closed record is fairmount avenue .
Output: yes
Command: and { eq { min { all_rows ; closed } ; 1983 } ; eq { hop { argmin { all_rows ; closed } ; station } ; fairmount avenue } }, interpretation: the average of the enrollment record of all rows is 651.8 .
Output: no
Command: round_eq { avg { all_rows ; enrollment } ; 651.8 }, interpretation: the average of the enrollment record of all rows is 651.8 .
Output: yes
Command: round_eq { avg { all_rows ; enrollment } ; 651.8 }, interpretation: select the rows whose winning party 2003 record fuzzily matches to liberal democrats . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } } ; 3 }, interpretation: select the rows whose winning party 2003 record fuzzily matches to liberal democrats . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } } ; 3 }, interpretation: select the rows whose bodyweight record is equal to 69.73 . there is only one such row in the table . the name record of this unqiue row is nelly rivera ( dom ) .
Output: no
Command: and { only { filter_eq { all_rows ; bodyweight ; 69.73 } } ; eq { hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name } ; nelly rivera ( dom ) } }, interpretation: select the rows whose bodyweight record is equal to 69.73 . there is only one such row in the table . the name record of this unqiue row is nelly rivera ( dom ) .
Output: yes
Command: and { only { filter_eq { all_rows ; bodyweight ; 69.73 } } ; eq { hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name } ; nelly rivera ( dom ) } }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is mcg .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; mcg }, interpretation: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is mcg .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; mcg }, interpretation: select the rows whose status record fuzzily matches to millennium trophy match . there is only one such row in the table . the opposing teams record of this unqiue row is ireland .
Output: no
Command: and { only { filter_eq { all_rows ; status ; millennium trophy match } } ; eq { hop { filter_eq { all_rows ; status ; millennium trophy match } ; opposing teams } ; ireland } }, interpretation: select the rows whose status record fuzzily matches to millennium trophy match . there is only one such row in the table . the opposing teams record of this unqiue row is ireland .
Output: yes
Command: and { only { filter_eq { all_rows ; status ; millennium trophy match } } ; eq { hop { filter_eq { all_rows ; status ; millennium trophy match } ; opposing teams } ; ireland } }, interpretation: select the rows whose attendance record is greater than 20,000 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_greater { all_rows ; attendance ; 20,000 } } ; 5 }, interpretation: select the rows whose attendance record is greater than 20,000 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_greater { all_rows ; attendance ; 20,000 } } ; 5 }, interpretation: the average of the crowd record of all rows is 12000 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 12000 }, interpretation: the average of the crowd record of all rows is 12000 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 12000 }, interpretation: select the rows whose studio record fuzzily matches to tristar pictures . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; studio ; tristar pictures } }, interpretation: select the rows whose studio record fuzzily matches to tristar pictures . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; studio ; tristar pictures } }, interpretation: the 1st minimum first elected record of all rows is 1904 . the incumbent record of the row with 1st minimum first elected record is j thomas heflin .
Output: no
Command: and { eq { nth_min { all_rows ; first elected ; 1 } ; 1904 } ; eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; j thomas heflin } }, interpretation: the 1st minimum first elected record of all rows is 1904 . the incumbent record of the row with 1st minimum first elected record is j thomas heflin .
Output: yes
Command: and { eq { nth_min { all_rows ; first elected ; 1 } ; 1904 } ; eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; j thomas heflin } }, interpretation: select the rows whose first elected record is equal to 1985 . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; first elected ; 1985 } }, interpretation: select the rows whose first elected record is equal to 1985 . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; first elected ; 1985 } }, interpretation: select the rows whose goals record is equal to 23 . there is only one such row in the table . the player record of this unqiue row is mehmet yılmaz .
Output: no
Command: and { only { filter_eq { all_rows ; goals ; 23 } } ; eq { hop { filter_eq { all_rows ; goals ; 23 } ; player } ; mehmet yılmaz } }, interpretation: select the rows whose goals record is equal to 23 . there is only one such row in the table . the player record of this unqiue row is mehmet yılmaz .
Output: yes
Command: and { only { filter_eq { all_rows ; goals ; 23 } } ; eq { hop { filter_eq { all_rows ; goals ; 23 } ; player } ; mehmet yılmaz } }, interpretation: select the rows whose bowling style record fuzzily matches to left arm slow chinaman . there is only one such row in the table . the player record of this unqiue row is michael bevan .
Output: no
Command: and { only { filter_eq { all_rows ; bowling style ; left arm slow chinaman } } ; eq { hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player } ; michael bevan } }, interpretation: select the rows whose bowling style record fuzzily matches to left arm slow chinaman . there is only one such row in the table . the player record of this unqiue row is michael bevan .
Output: yes
Command: and { only { filter_eq { all_rows ; bowling style ; left arm slow chinaman } } ; eq { hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player } ; michael bevan } }, interpretation: select the row whose first elected record of all rows is 4th minimum . the incumbent record of this row is robert aderholt .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent } ; robert aderholt }, interpretation: select the row whose first elected record of all rows is 4th minimum . the incumbent record of this row is robert aderholt .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent } ; robert aderholt }, interpretation: for the class records of all rows , most of them fuzzily match to 48 kg .
Output: no
Command: most_eq { all_rows ; class ; 48 kg }, interpretation: for the class records of all rows , most of them fuzzily match to 48 kg .
Output: yes
Command: most_eq { all_rows ; class ; 48 kg }, interpretation: for the category records of all rows , all of them fuzzily match to short film 2005 prix uip .
Output: no
Command: all_eq { all_rows ; category ; short film 2005 prix uip }, interpretation: for the category records of all rows , all of them fuzzily match to short film 2005 prix uip .
Output: yes
Command: all_eq { all_rows ; category ; short film 2005 prix uip }, interpretation: for the used as or integrated with records of all rows , most of them fuzzily match to software .
Output: no
Command: most_eq { all_rows ; used as or integrated with ; software }, interpretation: for the used as or integrated with records of all rows , most of them fuzzily match to software .
Output: yes
Command: most_eq { all_rows ; used as or integrated with ; software }, interpretation: select the rows whose team record fuzzily matches to pk racing . there is only one such row in the table . the name record of this unqiue row is max papis .
Output: no
Command: and { only { filter_eq { all_rows ; team ; pk racing } } ; eq { hop { filter_eq { all_rows ; team ; pk racing } ; name } ; max papis } }, interpretation: select the rows whose team record fuzzily matches to pk racing . there is only one such row in the table . the name record of this unqiue row is max papis .
Output: yes
Command: and { only { filter_eq { all_rows ; team ; pk racing } } ; eq { hop { filter_eq { all_rows ; team ; pk racing } ; name } ; max papis } }, interpretation: select the rows whose opponent in the final record fuzzily matches to adriano panatta . there is only one such row in the table . the championship record of this unqiue row is bologna , italy .
Output: no
Command: and { only { filter_eq { all_rows ; opponent in the final ; adriano panatta } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; adriano panatta } ; championship } ; bologna , italy } }, interpretation: select the rows whose opponent in the final record fuzzily matches to adriano panatta . there is only one such row in the table . the championship record of this unqiue row is bologna , italy .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent in the final ; adriano panatta } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; adriano panatta } ; championship } ; bologna , italy } }, interpretation: select the rows whose area ( km 2 ) record is less than 50 . select the row whose population record of these rows is 1st maximum . the common of record of this row is moncalieri .
Output: no
Command: eq { hop { nth_argmax { filter_less { all_rows ; area ( km 2 ) ; 50 } ; population ; 1 } ; common of } ; moncalieri }, interpretation: select the rows whose area ( km 2 ) record is less than 50 . select the row whose population record of these rows is 1st maximum . the common of record of this row is moncalieri .
Output: yes
Command: eq { hop { nth_argmax { filter_less { all_rows ; area ( km 2 ) ; 50 } ; population ; 1 } ; common of } ; moncalieri }, interpretation: for the party records of all rows , most of them fuzzily match to democratic - republican .
Output: no
Command: most_eq { all_rows ; party ; democratic - republican }, interpretation: select the rows whose team 1 record fuzzily matches to elche . take the 2nd leg record of this row . select the rows whose team 1 record fuzzily matches to numancia . take the 2nd leg record of this row . the first record fuzzily matches to the second record . the 2nd leg record of the first row is 0 - 2 . the 2nd leg record of the second row is 0 - 2 .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } } ; and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; 0 - 2 } ; eq { hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } ; 0 - 2 } } }, interpretation: select the rows whose team 1 record fuzzily matches to elche . take the 2nd leg record of this row . select the rows whose team 1 record fuzzily matches to numancia . take the 2nd leg record of this row . the first record fuzzily matches to the second record . the 2nd leg record of the first row is 0 - 2 . the 2nd leg record of the second row is 0 - 2 .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } } ; and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; 0 - 2 } ; eq { hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } ; 0 - 2 } } }, interpretation: the 2nd minimum date record of all rows is 31 may .
Output: no
Command: eq { nth_min { all_rows ; date ; 2 } ; 31 may }, interpretation: the 2nd minimum date record of all rows is 31 may .
Output: yes
Command: eq { nth_min { all_rows ; date ; 2 } ; 31 may }, interpretation: select the rows whose points record is greater than 20 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; points ; 20 } } ; 3 }, interpretation: select the rows whose points record is greater than 20 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; points ; 20 } } ; 3 }, interpretation: select the rows whose incumbent record fuzzily matches to frank t bow . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to louis stokes . take the first elected record of this row . the second record is 18 years larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; frank t bow } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; louis stokes } ; first elected } } ; -18 years }, interpretation: select the rows whose incumbent record fuzzily matches to frank t bow . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to louis stokes . take the first elected record of this row . the second record is 18 years larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; frank t bow } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; louis stokes } ; first elected } } ; -18 years }, interpretation: select the rows whose height in ft record is less than 6-0 . there is only one such row in the table . the player record of this unqiue row is turner , andre andre turner .
Output: no
Command: and { only { filter_less { all_rows ; height in ft ; 6-0 } } ; eq { hop { filter_less { all_rows ; height in ft ; 6-0 } ; player } ; turner , andre andre turner } }, interpretation: select the rows whose height in ft record is less than 6-0 . there is only one such row in the table . the player record of this unqiue row is turner , andre andre turner .
Output: yes
Command: and { only { filter_less { all_rows ; height in ft ; 6-0 } } ; eq { hop { filter_less { all_rows ; height in ft ; 6-0 } ; player } ; turner , andre andre turner } }, interpretation: for the site records of all rows , most of them fuzzily match to ames .
Output: no
Command: most_eq { all_rows ; site ; ames }, interpretation: for the site records of all rows , most of them fuzzily match to ames .
Output: yes
Command: most_eq { all_rows ; site ; ames }, interpretation: select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance } }, interpretation: select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance } }, interpretation: for the owner records of all rows , most of them fuzzily match to greentrains .
Output: no
Command: most_eq { all_rows ; owner ; greentrains }, interpretation: for the owner records of all rows , most of them fuzzily match to greentrains .
Output: yes
Command: most_eq { all_rows ; owner ; greentrains }, interpretation: select the rows whose date record fuzzily matches to nov 15 . take the cowboys points record of this row . select the rows whose date record fuzzily matches to nov 8 . take the cowboys points record of this row . the first record is 2 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; date ; nov 15 } ; cowboys points } ; hop { filter_eq { all_rows ; date ; nov 8 } ; cowboys points } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to nov 15 . take the cowboys points record of this row . select the rows whose date record fuzzily matches to nov 8 . take the cowboys points record of this row . the first record is 2 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; date ; nov 15 } ; cowboys points } ; hop { filter_eq { all_rows ; date ; nov 8 } ; cowboys points } } ; 2 }, interpretation: select the rows whose ignition timing record fuzzily matches to 180 - 540 . there is only one such row in the table . the engine record of this unqiue row is i4 ' long bang ' .
Output: no
Command: and { only { filter_eq { all_rows ; ignition timing ; 180 - 540 } } ; eq { hop { filter_eq { all_rows ; ignition timing ; 180 - 540 } ; engine } ; i4 ' long bang ' } }, interpretation: select the rows whose ignition timing record fuzzily matches to 180 - 540 . there is only one such row in the table . the engine record of this unqiue row is i4 ' long bang ' .
Output: yes
Command: and { only { filter_eq { all_rows ; ignition timing ; 180 - 540 } } ; eq { hop { filter_eq { all_rows ; ignition timing ; 180 - 540 } ; engine } ; i4 ' long bang ' } }, interpretation: select the rows whose owner record fuzzily matches to eternacom . there is only one such row in the table . the frequency record of this unqiue row is 103.5 fm .
Output: no
Command: and { only { filter_eq { all_rows ; owner ; eternacom } } ; eq { hop { filter_eq { all_rows ; owner ; eternacom } ; frequency } ; 103.5 fm } }, interpretation: select the rows whose owner record fuzzily matches to eternacom . there is only one such row in the table . the frequency record of this unqiue row is 103.5 fm .
Output: yes
Command: and { only { filter_eq { all_rows ; owner ; eternacom } } ; eq { hop { filter_eq { all_rows ; owner ; eternacom } ; frequency } ; 103.5 fm } }, interpretation: select the rows whose lost record is equal to 0 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; lost ; 0 } } ; 5 }, interpretation: select the rows whose lost record is equal to 0 . the number of such rows is 5 .
Output: yes