niryuu/sni-each-converted huggingface dataset:
Command: eq { count { filter_eq { all_rows ; lost ; 0 } } ; 5 }, interpretation: select the rows whose team record fuzzily matches to detroit . among these rows , select the rows whose high rebounds record fuzzily matches to shaquille o'neal . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; team ; detroit } ; high rebounds ; shaquille o'neal } } ; 3 }, interpretation: select the rows whose team record fuzzily matches to detroit . among these rows , select the rows whose high rebounds record fuzzily matches to shaquille o'neal . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; team ; detroit } ; high rebounds ; shaquille o'neal } } ; 3 }, interpretation: select the rows whose fate record fuzzily matches to wrecked . there is only one such row in the table . the name record of this unqiue row is magnet .
Output: no
Command: and { only { filter_eq { all_rows ; fate ; wrecked } } ; eq { hop { filter_eq { all_rows ; fate ; wrecked } ; name } ; magnet } }, interpretation: select the rows whose fate record fuzzily matches to wrecked . there is only one such row in the table . the name record of this unqiue row is magnet .
Output: yes
Command: and { only { filter_eq { all_rows ; fate ; wrecked } } ; eq { hop { filter_eq { all_rows ; fate ; wrecked } ; name } ; magnet } }, interpretation: select the row whose score record of all rows is maximum . the tie no record of this row is replay . the home team record of this row is southampton . the away team record of this row is sheffield wednesday .
Output: no
Command: and { eq { hop { argmax { all_rows ; score } ; tie no } ; replay } ; and { eq { hop { argmax { all_rows ; score } ; home team } ; southampton } ; eq { hop { argmax { all_rows ; score } ; away team } ; sheffield wednesday } } }, interpretation: select the row whose score record of all rows is maximum . the tie no record of this row is replay . the home team record of this row is southampton . the away team record of this row is sheffield wednesday .
Output: yes
Command: and { eq { hop { argmax { all_rows ; score } ; tie no } ; replay } ; and { eq { hop { argmax { all_rows ; score } ; home team } ; southampton } ; eq { hop { argmax { all_rows ; score } ; away team } ; sheffield wednesday } } }, interpretation: the average of the height record of all rows is 198.77 .
Output: no
Command: round_eq { avg { all_rows ; height } ; 198.77 }, interpretation: the average of the height record of all rows is 198.77 .
Output: yes
Command: round_eq { avg { all_rows ; height } ; 198.77 }, interpretation: for the nationality records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; nationality ; united states }, interpretation: select the rows whose position record fuzzily matches to power forward . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; power forward } } ; 3 }, interpretation: select the rows whose position record fuzzily matches to power forward . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; power forward } } ; 3 }, interpretation: select the row whose hosted 4 teams since record of all rows is minimum . the metropolitan area record of this row is detroit , michigan .
Output: no
Command: eq { hop { argmin { all_rows ; hosted 4 teams since } ; metropolitan area } ; detroit , michigan }, interpretation: select the row whose hosted 4 teams since record of all rows is minimum . the metropolitan area record of this row is detroit , michigan .
Output: yes
Command: eq { hop { argmin { all_rows ; hosted 4 teams since } ; metropolitan area } ; detroit , michigan }, interpretation: for the party records of all rows , all of them fuzzily match to democratic .
Output: no
Command: all_eq { all_rows ; party ; democratic }, interpretation: for the elevator records of all rows , all of them fuzzily match to urban vi .
Output: no
Command: all_eq { all_rows ; elevator ; urban vi }, interpretation: for the elevator records of all rows , all of them fuzzily match to urban vi .
Output: yes
Command: all_eq { all_rows ; elevator ; urban vi }, interpretation: for the score records of all rows , most of them are equal to 70 .
Output: no
Command: most_eq { all_rows ; score ; 70 }, interpretation: for the score records of all rows , most of them are equal to 70 .
Output: yes
Command: most_eq { all_rows ; score ; 70 }, interpretation: select the row whose duration record of all rows is 2nd maximum . the interregnum began record of this row is 20 october 1740 death of charles vi .
Output: no
Command: eq { hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began } ; 20 october 1740 death of charles vi }, interpretation: select the row whose duration record of all rows is 2nd maximum . the interregnum began record of this row is 20 october 1740 death of charles vi .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began } ; 20 october 1740 death of charles vi }, interpretation: select the rows whose school / club team record fuzzily matches to wisconsin . there is only one such row in the table . the player record of this unqiue row is todd nelson .
Output: no
Command: and { only { filter_eq { all_rows ; school / club team ; wisconsin } } ; eq { hop { filter_eq { all_rows ; school / club team ; wisconsin } ; player } ; todd nelson } }, interpretation: select the rows whose school / club team record fuzzily matches to wisconsin . there is only one such row in the table . the player record of this unqiue row is todd nelson .
Output: yes
Command: and { only { filter_eq { all_rows ; school / club team ; wisconsin } } ; eq { hop { filter_eq { all_rows ; school / club team ; wisconsin } ; player } ; todd nelson } }, interpretation: select the rows whose rd record is greater than or equal to 4 . select the row whose pick record of these rows is 2nd minimum . the player record of this row is matt butcher .
Output: no
Command: eq { hop { nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 } ; player } ; matt butcher }, interpretation: select the rows whose rd record is greater than or equal to 4 . select the row whose pick record of these rows is 2nd minimum . the player record of this row is matt butcher .
Output: yes
Command: eq { hop { nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 } ; player } ; matt butcher }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is detroit lions .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; detroit lions }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is detroit lions .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; detroit lions }, interpretation: select the rows whose 1st party record fuzzily matches to liberal . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; 1st party ; liberal } } ; 4 }, interpretation: select the rows whose 1st party record fuzzily matches to liberal . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 1st party ; liberal } } ; 4 }, interpretation: select the rows whose date record fuzzily matches to 30 august 1952 . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; 30 august 1952 } } ; 6 }, interpretation: select the rows whose date record fuzzily matches to 30 august 1952 . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; 30 august 1952 } } ; 6 }, interpretation: select the rows whose rebounds per game record is equal to 3.4 . there is only one such row in the table . the tournament record of this unqiue row is 2011 eurobasket .
Output: no
Command: and { only { filter_eq { all_rows ; rebounds per game ; 3.4 } } ; eq { hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament } ; 2011 eurobasket } }, interpretation: select the rows whose rebounds per game record is equal to 3.4 . there is only one such row in the table . the tournament record of this unqiue row is 2011 eurobasket .
Output: yes
Command: and { only { filter_eq { all_rows ; rebounds per game ; 3.4 } } ; eq { hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament } ; 2011 eurobasket } }, interpretation: select the rows whose tournament record fuzzily matches to baton rouge , us . take the date record of this row . select the rows whose tournament record fuzzily matches to granby , canada . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; tournament ; baton rouge , us } ; date } ; hop { filter_eq { all_rows ; tournament ; granby , canada } ; date } }, interpretation: select the rows whose tournament record fuzzily matches to baton rouge , us . take the date record of this row . select the rows whose tournament record fuzzily matches to granby , canada . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; tournament ; baton rouge , us } ; date } ; hop { filter_eq { all_rows ; tournament ; granby , canada } ; date } }, interpretation: select the row whose us viewers ( million ) record of all rows is 2nd maximum . the - record of this row is 1 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; - } ; 1 }, interpretation: select the row whose us viewers ( million ) record of all rows is 2nd maximum . the - record of this row is 1 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; - } ; 1 }, interpretation: select the rows whose 2009 record fuzzily matches to sf . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: no
Command: and { only { filter_eq { all_rows ; 2009 ; sf } } ; eq { hop { filter_eq { all_rows ; 2009 ; sf } ; tournament } ; us open } }, interpretation: select the rows whose 2009 record fuzzily matches to sf . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: yes
Command: and { only { filter_eq { all_rows ; 2009 ; sf } } ; eq { hop { filter_eq { all_rows ; 2009 ; sf } ; tournament } ; us open } }, interpretation: the average of the lost record of all rows is 7 .
Output: no
Command: round_eq { avg { all_rows ; lost } ; 7 }, interpretation: the average of the lost record of all rows is 7 .
Output: yes
Command: round_eq { avg { all_rows ; lost } ; 7 }, interpretation: the average of the weight record of all rows is 250.5 .
Output: no
Command: round_eq { avg { all_rows ; weight } ; 250.5 }, interpretation: the average of the weight record of all rows is 250.5 .
Output: yes
Command: round_eq { avg { all_rows ; weight } ; 250.5 }, interpretation: select the rows whose industry record fuzzily matches to oil and gas . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; industry ; oil and gas } } ; 4 }, interpretation: select the rows whose industry record fuzzily matches to oil and gas . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; industry ; oil and gas } } ; 4 }, interpretation: select the row whose weeks on top record of all rows is 1st maximum . the song record of this row is music .
Output: no
Command: eq { hop { nth_argmax { all_rows ; weeks on top ; 1 } ; song } ; music }, interpretation: select the row whose weeks on top record of all rows is 1st maximum . the song record of this row is music .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; weeks on top ; 1 } ; song } ; music }, interpretation: select the rows whose theme record fuzzily matches to 1970s dance music . there is only one such row in the table . the week record of this unqiue row is top 7 .
Output: no
Command: and { only { filter_eq { all_rows ; theme ; 1970s dance music } } ; eq { hop { filter_eq { all_rows ; theme ; 1970s dance music } ; week } ; top 7 } }, interpretation: select the rows whose theme record fuzzily matches to 1970s dance music . there is only one such row in the table . the week record of this unqiue row is top 7 .
Output: yes
Command: and { only { filter_eq { all_rows ; theme ; 1970s dance music } } ; eq { hop { filter_eq { all_rows ; theme ; 1970s dance music } ; week } ; top 7 } }, interpretation: select the rows whose name record fuzzily matches to jack riley . the sum of the games record of these rows is 375 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; name ; jack riley } ; games } ; 375 }, interpretation: select the rows whose name record fuzzily matches to jack riley . the sum of the games record of these rows is 375 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; name ; jack riley } ; games } ; 375 }, interpretation: for the visitor records of all rows , most of them fuzzily match to new jersey devils .
Output: no
Command: most_eq { all_rows ; visitor ; new jersey devils }, interpretation: for the visitor records of all rows , most of them fuzzily match to new jersey devils .
Output: yes
Command: most_eq { all_rows ; visitor ; new jersey devils }, interpretation: select the rows whose venue record fuzzily matches to away . the average of the attendance record of these rows is 1,692 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; venue ; away } ; attendance } ; 1,692 }, interpretation: select the rows whose venue record fuzzily matches to away . the average of the attendance record of these rows is 1,692 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; venue ; away } ; attendance } ; 1,692 }, interpretation: select the row whose entry date record of all rows is minimum . the single record of this row is meet me halfway .
Output: no
Command: eq { hop { argmin { all_rows ; entry date } ; single } ; meet me halfway }, interpretation: select the row whose entry date record of all rows is minimum . the single record of this row is meet me halfway .
Output: yes
Command: eq { hop { argmin { all_rows ; entry date } ; single } ; meet me halfway }, interpretation: select the rows whose top - 5 record is greater than 0 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { all_rows ; top - 5 ; 0 } } ; 2 }, interpretation: select the rows whose top - 5 record is greater than 0 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { all_rows ; top - 5 ; 0 } } ; 2 }, interpretation: select the row whose year record of all rows is 2nd maximum . the role record of this row is ahana sharma .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; role } ; ahana sharma }, interpretation: select the row whose year record of all rows is 2nd maximum . the role record of this row is ahana sharma .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; role } ; ahana sharma }, interpretation: select the rows whose result record does not match to re - elected . there is only one such row in the table . the incumbent record of this unqiue row is brooks hays .
Output: no
Command: and { only { filter_not_eq { all_rows ; result ; re - elected } } ; eq { hop { filter_not_eq { all_rows ; result ; re - elected } ; incumbent } ; brooks hays } }, interpretation: select the rows whose result record does not match to re - elected . there is only one such row in the table . the incumbent record of this unqiue row is brooks hays .
Output: yes
Command: and { only { filter_not_eq { all_rows ; result ; re - elected } } ; eq { hop { filter_not_eq { all_rows ; result ; re - elected } ; incumbent } ; brooks hays } }, interpretation: select the row whose points record of all rows is maximum . the artist record of this row is niamh kavanagh .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; artist } ; niamh kavanagh }, interpretation: select the row whose points record of all rows is maximum . the artist record of this row is niamh kavanagh .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; artist } ; niamh kavanagh }, interpretation: select the rows whose time / retired record fuzzily matches to + 6 laps . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; time / retired ; + 6 laps } } ; 3 }, interpretation: select the rows whose time / retired record fuzzily matches to + 6 laps . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; time / retired ; + 6 laps } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to w . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; w } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to w . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; w } } ; 2 }, interpretation: select the rows whose team 1 record fuzzily matches to as saint - étienne ( d1 ) . take the score record of this row . select the rows whose team 1 record fuzzily matches to girondins de bordeaux ( d1 ) . take the score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; team 1 ; as saint - étienne ( d1 ) } ; score } ; hop { filter_eq { all_rows ; team 1 ; girondins de bordeaux ( d1 ) } ; score } }, interpretation: select the rows whose team 1 record fuzzily matches to as saint - étienne ( d1 ) . take the score record of this row . select the rows whose team 1 record fuzzily matches to girondins de bordeaux ( d1 ) . take the score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; team 1 ; as saint - étienne ( d1 ) } ; score } ; hop { filter_eq { all_rows ; team 1 ; girondins de bordeaux ( d1 ) } ; score } }, interpretation: select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to ezekiel c gathings . take the first elected record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; incumbent ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; ezekiel c gathings } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to ezekiel c gathings . take the first elected record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; incumbent ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; ezekiel c gathings } ; first elected } }, interpretation: select the rows whose host record fuzzily matches to jack arute . there is only one such row in the table . the year record of this unqiue row is 2008 .
Output: no
Command: and { only { filter_eq { all_rows ; host ; jack arute } } ; eq { hop { filter_eq { all_rows ; host ; jack arute } ; year } ; 2008 } }, interpretation: select the rows whose host record fuzzily matches to jack arute . there is only one such row in the table . the year record of this unqiue row is 2008 .
Output: yes
Command: and { only { filter_eq { all_rows ; host ; jack arute } } ; eq { hop { filter_eq { all_rows ; host ; jack arute } ; year } ; 2008 } }, interpretation: select the rows whose scoring rank record fuzzily matches to n/a . there is only one such row in the table . the year record of this unqiue row is 2005 .
Output: no
Command: and { only { filter_eq { all_rows ; scoring rank ; n/a } } ; eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 } }, interpretation: select the rows whose scoring rank record fuzzily matches to n/a . there is only one such row in the table . the year record of this unqiue row is 2005 .
Output: yes
Command: and { only { filter_eq { all_rows ; scoring rank ; n/a } } ; eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 } }, interpretation: select the rows whose hk viewers record is greater than 2.20 million . there is only one such row in the table . the english title record of this unqiue row is beyond the realm of conscience .
Output: no
Command: and { only { filter_greater { all_rows ; hk viewers ; 2.20 million } } ; eq { hop { filter_greater { all_rows ; hk viewers ; 2.20 million } ; english title } ; beyond the realm of conscience } }, interpretation: select the rows whose hk viewers record is greater than 2.20 million . there is only one such row in the table . the english title record of this unqiue row is beyond the realm of conscience .
Output: yes
Command: and { only { filter_greater { all_rows ; hk viewers ; 2.20 million } } ; eq { hop { filter_greater { all_rows ; hk viewers ; 2.20 million } ; english title } ; beyond the realm of conscience } }, interpretation: select the rows whose station record fuzzily matches to turnham green . take the cancelled record of this row . select the rows whose station record fuzzily matches to crouch end . take the cancelled record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; station ; turnham green } ; cancelled } ; hop { filter_eq { all_rows ; station ; crouch end } ; cancelled } }, interpretation: select the rows whose station record fuzzily matches to turnham green . take the cancelled record of this row . select the rows whose station record fuzzily matches to crouch end . take the cancelled record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; station ; turnham green } ; cancelled } ; hop { filter_eq { all_rows ; station ; crouch end } ; cancelled } }, interpretation: select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo } } ; 1 }, interpretation: select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo } } ; 1 }, interpretation: select the rows whose game site record fuzzily matches to the meadowlands . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; game site ; the meadowlands } } ; 8 }, interpretation: select the rows whose game site record fuzzily matches to the meadowlands . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; game site ; the meadowlands } } ; 8 }, interpretation: select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose first elected record is equal to 1974 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; first elected ; 1974 } } ; 2 }, interpretation: select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose first elected record is equal to 1974 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; first elected ; 1974 } } ; 2 }, interpretation: select the row whose distance record of all rows is maximum . the stage record of this row is 2 .
Output: no
Command: eq { hop { argmax { all_rows ; distance } ; stage } ; 2 }, interpretation: select the row whose distance record of all rows is maximum . the stage record of this row is 2 .
Output: yes
Command: eq { hop { argmax { all_rows ; distance } ; stage } ; 2 }, interpretation: select the rows whose engine record fuzzily matches to judd v8 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: no
Command: and { only { filter_eq { all_rows ; engine ; judd v8 } } ; eq { hop { filter_eq { all_rows ; engine ; judd v8 } ; year } ; 1989 } }, interpretation: select the rows whose engine record fuzzily matches to judd v8 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: yes
Command: and { only { filter_eq { all_rows ; engine ; judd v8 } } ; eq { hop { filter_eq { all_rows ; engine ; judd v8 } ; year } ; 1989 } }, interpretation: select the rows whose location record fuzzily matches to tokyo , japan . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; location ; tokyo , japan } } ; 3 }, interpretation: select the rows whose location record fuzzily matches to tokyo , japan . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location ; tokyo , japan } } ; 3 }, interpretation: select the row whose debut record of all rows is 3rd maximum . the ring name record of this row is kyokushūhō kōki .
Output: no
Command: eq { hop { nth_argmax { all_rows ; debut ; 3 } ; ring name } ; kyokushūhō kōki }, interpretation: select the row whose debut record of all rows is 3rd maximum . the ring name record of this row is kyokushūhō kōki .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; debut ; 3 } ; ring name } ; kyokushūhō kōki }, interpretation: select the rows whose rating record is less than 3 . there is only one such row in the table . the ride record of this unqiue row is taxi jam .
Output: no
Command: and { only { filter_less { all_rows ; rating ; 3 } } ; eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam } }, interpretation: select the rows whose rating record is less than 3 . there is only one such row in the table . the ride record of this unqiue row is taxi jam .
Output: yes
Command: and { only { filter_less { all_rows ; rating ; 3 } } ; eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam } }, interpretation: select the rows whose earpads record fuzzily matches to comfort pads . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; earpads ; comfort pads } } ; 4 }, interpretation: select the rows whose earpads record fuzzily matches to comfort pads . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; earpads ; comfort pads } } ; 4 }, interpretation: the average of the number of hurricanes record of all rows is 4.3 .
Output: no
Command: round_eq { avg { all_rows ; number of hurricanes } ; 4.3 }, interpretation: the average of the number of hurricanes record of all rows is 4.3 .
Output: yes
Command: round_eq { avg { all_rows ; number of hurricanes } ; 4.3 }, interpretation: select the rows whose games played record is greater than 100 . there is only one such row in the table . the rival record of this unqiue row is georgia .
Output: no
Command: and { only { filter_greater { all_rows ; games played ; 100 } } ; eq { hop { filter_greater { all_rows ; games played ; 100 } ; rival } ; georgia } }, interpretation: select the rows whose games played record is greater than 100 . there is only one such row in the table . the rival record of this unqiue row is georgia .
Output: yes
Command: and { only { filter_greater { all_rows ; games played ; 100 } } ; eq { hop { filter_greater { all_rows ; games played ; 100 } ; rival } ; georgia } }, interpretation: for the competition or tour records of all rows , most of them fuzzily match to friendly .
Output: no
Command: most_eq { all_rows ; competition or tour ; friendly }, interpretation: for the competition or tour records of all rows , most of them fuzzily match to friendly .
Output: yes
Command: most_eq { all_rows ; competition or tour ; friendly }, interpretation: select the rows whose 50 m split record is less than 29.0 . among these rows , select the rows whose nationality record fuzzily matches to zimbabwe . there is only one such row in the table . the name record of this unqiue row is kirsty coventry .
Output: no
Command: and { only { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } } ; eq { hop { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } ; name } ; kirsty coventry } }, interpretation: select the rows whose 50 m split record is less than 29.0 . among these rows , select the rows whose nationality record fuzzily matches to zimbabwe . there is only one such row in the table . the name record of this unqiue row is kirsty coventry .
Output: yes
Command: and { only { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } } ; eq { hop { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } ; name } ; kirsty coventry } }, interpretation: select the rows whose year record is equal to 2013 . among these rows , select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; year ; 2013 } ; result ; won } } ; 3 }, interpretation: select the rows whose year record is equal to 2013 . among these rows , select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; year ; 2013 } ; result ; won } } ; 3 }, interpretation: for the result records of all rows , most of them fuzzily match to advanced .
Output: no
Command: most_eq { all_rows ; result ; advanced }, interpretation: for the result records of all rows , most of them fuzzily match to advanced .
Output: yes
Command: most_eq { all_rows ; result ; advanced }, interpretation: select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top - 5 record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } }, interpretation: the average of the crowd record of all rows is 17128 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 17128 }, interpretation: the average of the crowd record of all rows is 17128 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 17128 }, interpretation: the average of the attendance record of all rows is 43583 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 43583 }, interpretation: the average of the attendance record of all rows is 43583 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 43583 }, interpretation: select the rows whose capacity record is greater than 10000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; capacity ; 10000 } } ; 3 }, interpretation: select the rows whose capacity record is greater than 10000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; capacity ; 10000 } } ; 3 }, interpretation: select the row whose year record of all rows is 2nd maximum . the album title record of this row is realism .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; album title } ; realism }, interpretation: select the row whose year record of all rows is 2nd maximum . the album title record of this row is realism .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; album title } ; realism }, interpretation: select the rows whose result record does not match to retired . the number of such rows is 4 .
Output: no
Command: eq { count { filter_not_eq { all_rows ; result ; retired } } ; 4 }, interpretation: select the rows whose result record does not match to retired . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_not_eq { all_rows ; result ; retired } } ; 4 }, interpretation: select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 } } ; 2 }, interpretation: select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 } } ; 2 }, interpretation: select the row whose frequency record of all rows is maximum . the model number record of this row is turion 64 x2 tl - 68 .
Output: no
Command: eq { hop { argmax { all_rows ; frequency } ; model number } ; turion 64 x2 tl - 68 }, interpretation: select the row whose frequency record of all rows is maximum . the model number record of this row is turion 64 x2 tl - 68 .
Output: yes
Command: eq { hop { argmax { all_rows ; frequency } ; model number } ; turion 64 x2 tl - 68 }, interpretation: select the rows whose rider record is arbitrary . the number of such rows is 8 .
Output: no
Command: eq { count { filter_all { all_rows ; rider } } ; 8 }, interpretation: select the rows whose rider record is arbitrary . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_all { all_rows ; rider } } ; 8 }, interpretation: select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; location ; yankee stadium } } ; 3 }, interpretation: select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location ; yankee stadium } } ; 3 }, interpretation: select the rows whose language record fuzzily matches to norwegian . there is only one such row in the table . the song record of this unqiue row is sommer i palma .
Output: no
Command: and { only { filter_eq { all_rows ; language ; norwegian } } ; eq { hop { filter_eq { all_rows ; language ; norwegian } ; song } ; sommer i palma } }, interpretation: select the rows whose language record fuzzily matches to norwegian . there is only one such row in the table . the song record of this unqiue row is sommer i palma .
Output: yes
Command: and { only { filter_eq { all_rows ; language ; norwegian } } ; eq { hop { filter_eq { all_rows ; language ; norwegian } ; song } ; sommer i palma } }, interpretation: the 1st minimum date record of all rows is may 31 .
Output: no
Command: eq { nth_min { all_rows ; date ; 1 } ; may 31 }, interpretation: the 1st minimum date record of all rows is may 31 .
Output: yes
Command: eq { nth_min { all_rows ; date ; 1 } ; may 31 }, interpretation: select the rows whose candidate ( party ) record fuzzily matches to joseph estrada ( pmp ) . take the spent per voter ( php ) record of this row . select the rows whose candidate ( party ) record fuzzily matches to jejomar binay ( pdp - laban ) . take the spent per voter ( php ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; candidate ( party ) ; joseph estrada ( pmp ) } ; spent per voter ( php ) } ; hop { filter_eq { all_rows ; candidate ( party ) ; jejomar binay ( pdp - laban ) } ; spent per voter ( php ) } }, interpretation: select the rows whose candidate ( party ) record fuzzily matches to joseph estrada ( pmp ) . take the spent per voter ( php ) record of this row . select the rows whose candidate ( party ) record fuzzily matches to jejomar binay ( pdp - laban ) . take the spent per voter ( php ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; candidate ( party ) ; joseph estrada ( pmp ) } ; spent per voter ( php ) } ; hop { filter_eq { all_rows ; candidate ( party ) ; jejomar binay ( pdp - laban ) } ; spent per voter ( php ) } }, interpretation: the maximum best finish record of all rows is t - 17 . the year record of the row with superlative best finish record is 2000 .
Output: no
Command: and { eq { max { all_rows ; best finish } ; t - 17 } ; eq { hop { argmax { all_rows ; best finish } ; year } ; 2000 } }, interpretation: the maximum best finish record of all rows is t - 17 . the year record of the row with superlative best finish record is 2000 .
Output: yes
Command: and { eq { max { all_rows ; best finish } ; t - 17 } ; eq { hop { argmax { all_rows ; best finish } ; year } ; 2000 } }, interpretation: select the rows whose home team score record is less than 15 . among these rows , select the rows whose crowd record is greater than 15000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { filter_less { all_rows ; home team score ; 15 } ; crowd ; 15000 } } ; 3 }, interpretation: select the rows whose home team score record is less than 15 . among these rows , select the rows whose crowd record is greater than 15000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { filter_less { all_rows ; home team score ; 15 } ; crowd ; 15000 } } ; 3 }, interpretation: for the team name records of all rows , most of them fuzzily match to kemba racing .
Output: no
Command: most_eq { all_rows ; team name ; kemba racing }, interpretation: for the team name records of all rows , most of them fuzzily match to kemba racing .
Output: yes
Command: most_eq { all_rows ; team name ; kemba racing }, interpretation: select the rows whose player record fuzzily matches to craig stadler . take the money record of this row . select the rows whose player record fuzzily matches to fred couples . take the money record of this row . the first record is 5863 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } } ; 5863 }, interpretation: select the rows whose player record fuzzily matches to craig stadler . take the money record of this row . select the rows whose player record fuzzily matches to fred couples . take the money record of this row . the first record is 5863 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } } ; 5863 }, interpretation: select the rows whose opponent record fuzzily matches to troy nelson . take the round record of this row . select the rows whose opponent record fuzzily matches to shelton barnes . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; troy nelson } ; round } ; hop { filter_eq { all_rows ; opponent ; shelton barnes } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to troy nelson . take the round record of this row . select the rows whose opponent record fuzzily matches to shelton barnes . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; troy nelson } ; round } ; hop { filter_eq { all_rows ; opponent ; shelton barnes } ; round } }, interpretation: select the row whose fatalities record of all rows is maximum . the date record of this row is 3 march 1974 .
Output: no
Command: eq { hop { argmax { all_rows ; fatalities } ; date } ; 3 march 1974 }, interpretation: select the row whose fatalities record of all rows is maximum . the date record of this row is 3 march 1974 .
Output: yes
Command: eq { hop { argmax { all_rows ; fatalities } ; date } ; 3 march 1974 }, interpretation: select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; hard } } ; 4 }, interpretation: select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; surface ; hard } } ; 4 }, interpretation: select the rows whose position record fuzzily matches to test driver . there is only one such row in the table . the series record of this unqiue row is formula one .
Output: no
Command: and { only { filter_eq { all_rows ; position ; test driver } } ; eq { hop { filter_eq { all_rows ; position ; test driver } ; series } ; formula one } }, interpretation: select the rows whose position record fuzzily matches to test driver . there is only one such row in the table . the series record of this unqiue row is formula one .
Output: yes
Command: and { only { filter_eq { all_rows ; position ; test driver } } ; eq { hop { filter_eq { all_rows ; position ; test driver } ; series } ; formula one } }, interpretation: the sum of the crowd record of all rows is 129,800 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 129,800 }, interpretation: the sum of the crowd record of all rows is 129,800 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 129,800 }, interpretation: the average of the wins record of all rows is .12 .
Output: no
Command: round_eq { avg { all_rows ; wins } ; .12 }, interpretation: the average of the wins record of all rows is .12 .
Output: yes
Command: round_eq { avg { all_rows ; wins } ; .12 }, interpretation: select the rows whose coach record fuzzily matches to steve yoder . take the record record of this row . select the rows whose coach record fuzzily matches to bill cofield . take the record record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; coach ; steve yoder } ; record } ; hop { filter_eq { all_rows ; coach ; bill cofield } ; record } }, interpretation: select the rows whose coach record fuzzily matches to steve yoder . take the record record of this row . select the rows whose coach record fuzzily matches to bill cofield . take the record record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; coach ; steve yoder } ; record } ; hop { filter_eq { all_rows ; coach ; bill cofield } ; record } }, interpretation: for the wins records of all rows , all of them are equal to 0 .
Output: no
Command: all_eq { all_rows ; wins ; 0 }, interpretation: select the row whose games record of all rows is maximum . the season record of this row is 2007 .
Output: no
Command: eq { hop { argmax { all_rows ; games } ; season } ; 2007 }, interpretation: select the row whose games record of all rows is maximum . the season record of this row is 2007 .
Output: yes
Command: eq { hop { argmax { all_rows ; games } ; season } ; 2007 }, interpretation: for the result records of all rows , most of them fuzzily match to w .
Output: no
Command: most_eq { all_rows ; result ; w }, interpretation: for the written by records of all rows , all of them fuzzily match to michael hirst .
Output: no
Command: all_eq { all_rows ; written by ; michael hirst }, interpretation: for the written by records of all rows , all of them fuzzily match to michael hirst .
Output: yes
Command: all_eq { all_rows ; written by ; michael hirst }, interpretation: select the rows whose gold record is equal to 13 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; gold ; 13 } } ; 2 }, interpretation: select the rows whose gold record is equal to 13 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; gold ; 13 } } ; 2 }, interpretation: select the rows whose official name record fuzzily matches to saint george . take the population record of this row . select the rows whose official name record fuzzily matches to saint andrews . take the population record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; official name ; saint george } ; population } ; hop { filter_eq { all_rows ; official name ; saint andrews } ; population } }, interpretation: select the rows whose official name record fuzzily matches to saint george . take the population record of this row . select the rows whose official name record fuzzily matches to saint andrews . take the population record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; official name ; saint george } ; population } ; hop { filter_eq { all_rows ; official name ; saint andrews } ; population } }, interpretation: select the rows whose year record is equal to 1979 . the sum of the result record of these rows is 59 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; year ; 1979 } ; result } ; 59 }, interpretation: select the rows whose year record is equal to 1979 . the sum of the result record of these rows is 59 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; year ; 1979 } ; result } ; 59 }, interpretation: select the rows whose assists record is equal to 40 . the number of such rows is 13 .
Output: no
Command: eq { count { filter_eq { all_rows ; assists ; 40 } } ; 13 }, interpretation: select the rows whose assists record is equal to 40 . the number of such rows is 13 .
Output: yes
Command: eq { count { filter_eq { all_rows ; assists ; 40 } } ; 13 }, interpretation: select the rows whose pilot record fuzzily matches to and . there is only one such row in the table . the vehicle record of this unqiue row is schempp - hirth nimbus - 4dm .
Output: no
Command: and { only { filter_eq { all_rows ; pilot ; and } } ; eq { hop { filter_eq { all_rows ; pilot ; and } ; vehicle } ; schempp - hirth nimbus - 4dm } }, interpretation: select the rows whose pilot record fuzzily matches to and . there is only one such row in the table . the vehicle record of this unqiue row is schempp - hirth nimbus - 4dm .
Output: yes
Command: and { only { filter_eq { all_rows ; pilot ; and } } ; eq { hop { filter_eq { all_rows ; pilot ; and } ; vehicle } ; schempp - hirth nimbus - 4dm } }, interpretation: select the rows whose opponent record fuzzily matches to los angeles rams . the average of the attendance record of these rows is 68,437 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; opponent ; los angeles rams } ; attendance } ; 68,437 }, interpretation: select the rows whose opponent record fuzzily matches to los angeles rams . the average of the attendance record of these rows is 68,437 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; opponent ; los angeles rams } ; attendance } ; 68,437 }, interpretation: for the entrant records of all rows , most of them fuzzily match to clarke - mordaunt - guthrie .
Output: no
Command: most_eq { all_rows ; entrant ; clarke - mordaunt - guthrie }, interpretation: for the entrant records of all rows , most of them fuzzily match to clarke - mordaunt - guthrie .
Output: yes
Command: most_eq { all_rows ; entrant ; clarke - mordaunt - guthrie }, interpretation: select the row whose date record of all rows is 1st minimum . the opponent record of this row is cincinnati bengals .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; cincinnati bengals }, interpretation: select the row whose date record of all rows is 1st minimum . the opponent record of this row is cincinnati bengals .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; cincinnati bengals }, interpretation: for the begin records of all rows , most of them are less than 2009 .
Output: no
Command: most_less { all_rows ; begin ; 2009 }, interpretation: for the begin records of all rows , most of them are less than 2009 .
Output: yes
Command: most_less { all_rows ; begin ; 2009 }, interpretation: for the event records of all rows , most of them do not match to team competitions .
Output: no
Command: most_not_eq { all_rows ; event ; team competitions }, interpretation: for the event records of all rows , most of them do not match to team competitions .
Output: yes