niryuu/sni-each-converted huggingface dataset:
select the rows whose county record fuzzily matches to oppland . there is only one such row in the table . the name record of this unqiue row is galdhøpiggen .
Output: and { only { filter_eq { all_rows ; county ; oppland } } ; eq { hop { filter_eq { all_rows ; county ; oppland } ; name } ; galdhøpiggen } }
for the kitmaker records of all rows , most of them do not match to n / a .
Output: most_not_eq { all_rows ; kitmaker ; n / a }
for the character records of all rows , most of them fuzzily match to craven .
Output: most_eq { all_rows ; character ; craven }
select the rows whose december record is greater than 15 . among these rows , select the rows whose score record fuzzily matches to 4-2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; december ; 15 } ; score ; 4-2 } } ; 2 }
select the rows whose away team record fuzzily matches to melbourne . the average of the crowd record of these rows is 24500 .
Output: round_eq { avg { filter_eq { all_rows ; away team ; melbourne } ; crowd } ; 24500 }
select the rows whose result record fuzzily matches to draw . there is only one such row in the table . the venue record of this unqiue row is sydney cricket ground .
Output: and { only { filter_eq { all_rows ; result ; draw } } ; eq { hop { filter_eq { all_rows ; result ; draw } ; venue } ; sydney cricket ground } }
select the rows whose chassis record fuzzily matches to hwm 53 . there is only one such row in the table . the year record of this unqiue row is 1953 .
Output: and { only { filter_eq { all_rows ; chassis ; hwm 53 } } ; eq { hop { filter_eq { all_rows ; chassis ; hwm 53 } ; year } ; 1953 } }
for the format records of all rows , all of them fuzzily match to news / talk .
Output: all_eq { all_rows ; format ; news / talk }
for the land ( sqmi ) records of all rows , most of them are less than 40 .
Output: most_less { all_rows ; land ( sqmi ) ; 40 }
select the rows whose launched record fuzzily matches to 1973 . among these rows , select the rows whose destination record fuzzily matches to mars . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; launched ; 1973 } ; destination ; mars } } ; 3 }
select the rows whose earnings record is less than 560000 . there is only one such row in the table . the player record of this unqiue row is gene littler .
Output: and { only { filter_less { all_rows ; earnings ; 560000 } } ; eq { hop { filter_less { all_rows ; earnings ; 560000 } ; player } ; gene littler } }
the average of the time record of all rows is 5.603 .
Output: round_eq { avg { all_rows ; time } ; 5.603 }
select the rows whose visitor record fuzzily matches to spurs . select the row whose attendance record of these rows is 1st maximum . the home record of this row is jazz .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 1 } ; home } ; jazz }
select the rows whose country record fuzzily matches to soviet union . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; country ; soviet union } } ; 7 }
select the rows whose women 's singles record fuzzily matches to sara persson . there is only one such row in the table . the year record of this unqiue row is 2006 .
Output: and { only { filter_eq { all_rows ; women 's singles ; sara persson } } ; eq { hop { filter_eq { all_rows ; women 's singles ; sara persson } ; year } ; 2006 } }
select the rows whose manner of departure record fuzzily matches to resigned . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; resigned } } ; 2 }
select the row whose points record of all rows is maximum . the november record of this row is 29 .
Output: eq { hop { argmax { all_rows ; points } ; november } ; 29 }
the 2nd minimum year record of all rows is 1994 . the men 's singles record of the row with 2nd minimum year record is juraj brestovský .
Output: and { eq { nth_min { all_rows ; year ; 2 } ; 1994 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; men 's singles } ; juraj brestovský } }
the average of the attendance record of all rows is 45585 .
Output: round_eq { avg { all_rows ; attendance } ; 45585 }
select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .
Output: eq { count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } } ; 5 }
select the rows whose platform ( s ) record fuzzily matches to gamecube . there is only one such row in the table . the game record of this unqiue row is resident evil 4 .
Output: and { only { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 } }
select the rows whose current club record fuzzily matches to bot turów . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; current club ; bot turów } } ; 3 }
select the rows whose county record fuzzily matches to oppland . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; county ; oppland } } ; 2 }
select the row whose time record of all rows is minimum . the name record of this row is tyson gay .
Output: eq { hop { argmin { all_rows ; time } ; name } ; tyson gay }
for the location attendance records of all rows , most of them fuzzily match to amway arena .
Output: most_eq { all_rows ; location attendance ; amway arena }
select the rows whose score record fuzzily matches to 2 - 0 . there is only one such row in the table . the home team record of this unqiue row is eastern team a .
Output: and { only { filter_eq { all_rows ; score ; 2 - 0 } } ; eq { hop { filter_eq { all_rows ; score ; 2 - 0 } ; home team } ; eastern team a } }
select the rows whose spacecraft record fuzzily matches to venera . the sum of the time elapsed record of these rows is 205 .
Output: round_eq { sum { filter_eq { all_rows ; spacecraft ; venera } ; time elapsed } ; 205 }
select the rows whose region record fuzzily matches to canada . there is only one such row in the table . the catalog record of this unqiue row is b001150702 .
Output: and { only { filter_eq { all_rows ; region ; canada } } ; eq { hop { filter_eq { all_rows ; region ; canada } ; catalog } ; b001150702 } }
select the rows whose name record fuzzily matches to keutenberg . take the kilometer record of this row . select the rows whose name record fuzzily matches to fromberg . take the kilometer record of this row . the first record is 4 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; keutenberg } ; kilometer } ; hop { filter_eq { all_rows ; name ; fromberg } ; kilometer } } ; 4 }
select the rows whose name dates record fuzzily matches to anne , princess royal 1709 - 1759 . take the date married record of this row . select the rows whose name dates record fuzzily matches to charlotte , princess royal 1766 - 1828 . take the date married record of this row . the second record is 63 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name dates ; anne , princess royal 1709 - 1759 } ; date married } ; hop { filter_eq { all_rows ; name dates ; charlotte , princess royal 1766 - 1828 } ; date married } } ; -63 }
select the rows whose builder record fuzzily matches to general dynamics , quincy . select the row whose commissioned - decommissioned record of these rows is 1st minimum . the ship record of this row is wichita .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; builder ; general dynamics , quincy } ; commissioned - decommissioned ; 1 } ; ship } ; wichita }
select the rows whose round record fuzzily matches to qf . among these rows , select the rows whose surface record fuzzily matches to clay . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; round ; qf } ; surface ; clay } } ; 2 }
the average of the points record of all rows is 42.91 .
Output: round_eq { avg { all_rows ; points } ; 42.91 }
the 4th maximum points record of all rows is 6 .
Output: eq { nth_max { all_rows ; points ; 4 } ; 6 }
select the rows whose label record fuzzily matches to b ' in music . select the row whose release date record of these rows is minimum . the english title record of this row is kissing the future of love .
Output: eq { hop { argmin { filter_eq { all_rows ; label ; b ' in music } ; release date } ; english title } ; kissing the future of love }
select the rows whose championship record fuzzily matches to us open . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; championship ; us open } } ; 6 }
the sum of the races record of all rows is 190 .
Output: round_eq { sum { all_rows ; races } ; 190 }
select the rows whose opponent record fuzzily matches to jason st louis . take the time record of this row . select the rows whose opponent record fuzzily matches to mike swick . take the time record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; jason st louis } ; time } ; hop { filter_eq { all_rows ; opponent ; mike swick } ; time } }
select the row whose area ( km 2 ) record of all rows is maximum . the main town record of this row is adampan .
Output: eq { hop { argmax { all_rows ; area ( km 2 ) } ; main town } ; adampan }
select the rows whose premiere record fuzzily matches to 14 august . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; premiere ; 14 august } } ; 4 }
select the rows whose team record fuzzily matches to pescarolo sport . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; team ; pescarolo sport } } ; 2 }
the average of the attendance record of all rows is 24652 .
Output: round_eq { avg { all_rows ; attendance } ; 24652 }
select the rows whose film record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; film } } ; 6 }
select the rows whose location record fuzzily matches to england . there is only one such row in the table . the name record of this unqiue row is proraphidia gomezi .
Output: and { only { filter_eq { all_rows ; location ; england } } ; eq { hop { filter_eq { all_rows ; location ; england } ; name } ; proraphidia gomezi } }
select the rows whose reason for change record fuzzily matches to failure to elect . there is only one such row in the table . the state ( class ) record of this unqiue row is tennessee ( 2 ) .
Output: and { only { filter_eq { all_rows ; reason for change ; failure to elect } } ; eq { hop { filter_eq { all_rows ; reason for change ; failure to elect } ; state ( class ) } ; tennessee ( 2 ) } }
for the high points records of all rows , most of them fuzzily match to parker .
Output: most_eq { all_rows ; high points ; parker }
select the rows whose points record is equal to 17 . there is only one such row in the table . the team record of this unqiue row is antártica .
Output: and { only { filter_eq { all_rows ; points ; 17 } } ; eq { hop { filter_eq { all_rows ; points ; 17 } ; team } ; antártica } }
select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } }
select the row whose frequency record of all rows is maximum . the callsign record of this row is kgbt .
Output: eq { hop { argmax { all_rows ; frequency } ; callsign } ; kgbt }
select the rows whose country record fuzzily matches to italy . there is only one such row in the table .
Output: only { filter_eq { all_rows ; country ; italy } }
select the rows whose goalkeeper record fuzzily matches to matías garavano . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to álvaro campos . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; goalkeeper ; matías garavano } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; álvaro campos } ; goals } }
the average of the weight ( kg ) record of all rows is 54.29 .
Output: round_eq { avg { all_rows ; weight ( kg ) } ; 54.29 }
for the won records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; won ; 10 }
select the row whose purse record of all rows is maximum . the year record of this row is 2008 .
Output: eq { hop { argmax { all_rows ; purse } ; year } ; 2008 }
for the license records of all rows , most of them fuzzily match to proprietary .
Output: most_eq { all_rows ; license ; proprietary }
select the rows whose high rebounds record fuzzily matches to paul millsap . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; high rebounds ; paul millsap } } ; 2 }
select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record . the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .
Output: and { not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } } ; and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } } }
select the rows whose games record is equal to 14 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; games ; 14 } } ; 3 }
select the rows whose owned since record is equal to 2011 . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; owned since ; 2011 } } ; 9 }
select the rows whose points record is equal to 0 . there is only one such row in the table . the artist record of this unqiue row is photogenique .
Output: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; artist } ; photogenique } }
the sum of the winners share record of all rows is 444286 .
Output: round_eq { sum { all_rows ; winners share } ; 444286 }
select the rows whose position record fuzzily matches to db . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; db } } ; 2 }
select the rows whose district record fuzzily matches to sagar . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; district ; sagar } } ; 3 }
the sum of the attendance record of all rows is 741290 .
Output: round_eq { sum { all_rows ; attendance } ; 741290 }
for the pick records of all rows , all of them are equal to 2 .
Output: all_eq { all_rows ; pick ; 2 }
select the rows whose year record fuzzily matches to 1995 . take the pts record of this row . select the rows whose year record fuzzily matches to 1991 . take the pts record of this row . the first record is greater than the second record . the pts record of the first row is 13 . the pts record of the second row is 1 .
Output: and { greater { hop { filter_eq { all_rows ; year ; 1995 } ; pts } ; hop { filter_eq { all_rows ; year ; 1991 } ; pts } } ; and { eq { hop { filter_eq { all_rows ; year ; 1995 } ; pts } ; 13 } ; eq { hop { filter_eq { all_rows ; year ; 1991 } ; pts } ; 1 } } }
select the rows whose studio ( s ) record fuzzily matches to red chillies entertainment . there is only one such row in the table . the movie record of this unqiue row is chennai express .
Output: and { only { filter_eq { all_rows ; studio ( s ) ; red chillies entertainment } } ; eq { hop { filter_eq { all_rows ; studio ( s ) ; red chillies entertainment } ; movie } ; chennai express } }
for the date records of all rows , all of them fuzzily match to may .
Output: all_eq { all_rows ; date ; may }
select the rows whose category record fuzzily matches to helicopter . take the date record of this row . select the rows whose category record fuzzily matches to glider ( sailplane ) . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; category ; helicopter } ; date } ; hop { filter_eq { all_rows ; category ; glider ( sailplane ) } ; date } }
select the rows whose race record is equal to 28 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; race ; 28 } } ; 2 }
the average of the viewing figure record of all rows is 7.2 .
Output: round_eq { avg { all_rows ; viewing figure } ; 7.2 }
select the rows whose directed by record fuzzily matches to jon cassar . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; directed by ; jon cassar } } ; 5 }
select the rows whose artist record fuzzily matches to arnold nogy . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; artist ; arnold nogy } } ; 3 }
the average of the score record of all rows is 71 .
Output: round_eq { avg { all_rows ; score } ; 71 }
select the rows whose total record is greater than 100 . there is only one such row in the table . the nation record of this unqiue row is united states .
Output: and { only { filter_greater { all_rows ; total ; 100 } } ; eq { hop { filter_greater { all_rows ; total ; 100 } ; nation } ; united states } }
for the region 4 records of all rows , most of them do not match to n / a .
Output: most_not_eq { all_rows ; region 4 ; n / a }
the maximum score record of all rows is 5 - 0 .
Output: eq { max { all_rows ; score } ; 5 - 0 }
for the height records of all rows , most of them are less than 190 .
Output: most_less { all_rows ; height ; 190 }
the average of the games played record of all rows is 7.6 .
Output: round_eq { avg { all_rows ; games played } ; 7.6 }
select the rows whose ship name record fuzzily matches to ashantian . take the tonnage ( grt ) record of this row . select the rows whose ship name record fuzzily matches to manchester brigade . take the tonnage ( grt ) record of this row . the second record is 1125 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; ship name ; ashantian } ; tonnage ( grt ) } ; hop { filter_eq { all_rows ; ship name ; manchester brigade } ; tonnage ( grt ) } } ; -1125 }
select the row whose date record of all rows is 2nd minimum . the winning driver record of this row is arthur duray .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; winning driver } ; arthur duray }
select the rows whose runner - up record fuzzily matches to dynamo moscow . take the season record of this row . select the rows whose runner - up record fuzzily matches to lokomotiv moscow . take the season record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; runner - up ; dynamo moscow } ; season } ; hop { filter_eq { all_rows ; runner - up ; lokomotiv moscow } ; season } }
for the of weeks records of all rows , most of them are equal to 13 .
Output: most_eq { all_rows ; of weeks ; 13 }
select the rows whose tv network record fuzzily matches to cbs . the number of such rows is 12 .
Output: eq { count { filter_eq { all_rows ; tv network ; cbs } } ; 12 }
the average of the balls record of all rows is 670 .
Output: round_eq { avg { all_rows ; balls } ; 670 }
select the rows whose result record fuzzily matches to w . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; result ; w } } ; 5 }
the average of the away team score record of all rows is 10.86 .
Output: round_eq { avg { all_rows ; away team score } ; 10.86 }
select the rows whose bsu head coach record fuzzily matches to bus conner . take the year record of this row . select the rows whose bsu head coach record fuzzily matches to greg graham . take the year record of this row . the second record is 32 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; bsu head coach ; bus conner } ; year } ; hop { filter_eq { all_rows ; bsu head coach ; greg graham } ; year } } ; -32 years }
select the row whose year born record of all rows is 1st minimum . the player record of this row is sacha giffa .
Output: eq { hop { nth_argmin { all_rows ; year born ; 1 } ; player } ; sacha giffa }
select the rows whose miss international record fuzzily matches to lara quigaman . take the year record of this row . select the rows whose miss international record fuzzily matches to daniela di giacomo . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; miss international ; lara quigaman } ; year } ; hop { filter_eq { all_rows ; miss international ; daniela di giacomo } ; year } }
select the row whose entries record of all rows is maximum . the driver record of this row is rubens barrichello .
Output: eq { hop { argmax { all_rows ; entries } ; driver } ; rubens barrichello }
select the rows whose event record fuzzily matches to senior race . there is only one such row in the table . the year record of this unqiue row is 2008 .
Output: and { only { filter_eq { all_rows ; event ; senior race } } ; eq { hop { filter_eq { all_rows ; event ; senior race } ; year } ; 2008 } }
select the rows whose leading scorer record fuzzily matches to tony parker . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; leading scorer ; tony parker } } ; 4 }
for the year born records of all rows , most of them are greater than 1979 .
Output: most_greater { all_rows ; year born ; 1979 }
select the rows whose result record fuzzily matches to win . for the wildcats points records of these rows , most of them are greater than 20 .
Output: most_greater { filter_eq { all_rows ; result ; win } ; wildcats points ; 20 }
select the rows whose 1989 record fuzzily matches to 3r . there is only one such row in the table . the tournament record of this unqiue row is wimbledon .
Output: and { only { filter_eq { all_rows ; 1989 ; 3r } } ; eq { hop { filter_eq { all_rows ; 1989 ; 3r } ; tournament } ; wimbledon } }
select the rows whose us viewers ( in millions ) record is less than 8 . there is only one such row in the table . the no by series record of this unqiue row is 6 .
Output: and { only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } } ; eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 } }
select the rows whose type record does not match to public . the average of the enrollment record of these rows is 4,523 .
Output: round_eq { avg { filter_not_eq { all_rows ; type ; public } ; enrollment } ; 4,523 }
select the row whose us viewers ( millions ) record of all rows is maximum . the title record of this row is bea stays in the picture .
Output: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; title } ; bea stays in the picture }
select the rows whose habitat type record fuzzily matches to forest steppe . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; habitat type ; forest steppe } } ; 8 }
the average of the td 's record of all rows is 15 .
Output: round_eq { avg { all_rows ; td 's } ; 15 }
select the rows whose population record is greater than 50000 . among these rows , select the rows whose altitude ( mslm ) record is less than 300 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_greater { all_rows ; population ; 50000 } ; altitude ( mslm ) ; 300 } } ; 2 }
select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is fitzroy .
Output: eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; fitzroy }
select the rows whose position record fuzzily matches to forward . among these rows , select the rows whose years in orlando record is greater than or equal to 2000 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { filter_eq { all_rows ; position ; forward } ; years in orlando ; 2000 } } ; 3 }
select the rows whose opponent record fuzzily matches to gothenburg . among these rows , select the rows whose date record fuzzily matches to may . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; opponent ; gothenburg } ; date ; may } } ; 2 }
the average of the enrollment record of all rows is 1259.8 .
Output: round_eq { avg { all_rows ; enrollment } ; 1259.8 }
select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 9 }
select the rows whose opponent record fuzzily matches to chris haseman . take the round record of this row . select the rows whose opponent record fuzzily matches to wanderlei silva . take the round record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; opponent ; chris haseman } ; round } ; hop { filter_eq { all_rows ; opponent ; wanderlei silva } ; round } } ; 1 }
select the rows whose the world university rankings 2012 - 13 record is less than 300 . there is only one such row in the table . the university record of this unqiue row is queensland university of technology .
Output: and { only { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } } ; eq { hop { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } ; university } ; queensland university of technology } }
select the row whose original air date record of all rows is 5th minimum . the series record of this row is 5 .
Output: eq { hop { nth_argmin { all_rows ; original air date ; 5 } ; series } ; 5 }
select the rows whose winning driver record fuzzily matches to craig baird . among these rows , select the rows whose location record fuzzily matches to melbourne , victoria . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; winning driver ; craig baird } ; location ; melbourne , victoria } } ; 2 }
select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the vacator record of this unqiue row is ross bass ( d ) .
Output: and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; vacator } ; ross bass ( d ) } }
for the became consort records of all rows , most of them fuzzily match to husband 's ascession .
Output: most_eq { all_rows ; became consort ; husband 's ascession }
select the row whose 2006 - 10 record of all rows is maximum . the state record of this row is bihar .
Output: eq { hop { argmax { all_rows ; 2006 - 10 } ; state } ; bihar }
select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; tournament ; brazil } } ; 3 }
select the rows whose height in ft record is greater than or equal to 7 - 0 . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { all_rows ; height in ft ; 7 - 0 } } ; 2 }
select the rows whose winner record fuzzily matches to russell ingall . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; winner ; russell ingall } } ; 3 }
select the rows whose result record fuzzily matches to retired democratic hold . there is only one such row in the table .
Output: only { filter_eq { all_rows ; result ; retired democratic hold } }
select the rows whose colony record fuzzily matches to chandernagore . take the de facto transfer record of this row . select the rows whose colony record fuzzily matches to karikal . take the de facto transfer record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; colony ; chandernagore } ; de facto transfer } ; hop { filter_eq { all_rows ; colony ; karikal } ; de facto transfer } }
select the row whose capacity record of all rows is minimum . the stadium record of this row is billesley common .
Output: eq { hop { argmin { all_rows ; capacity } ; stadium } ; billesley common }
select the rows whose music record fuzzily matches to george gershwin . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; music ; george gershwin } } ; 4 }
select the rows whose drawn record is equal to 2 . there is only one such row in the table . the club record of this unqiue row is abercynon rfc .
Output: and { only { filter_eq { all_rows ; drawn ; 2 } } ; eq { hop { filter_eq { all_rows ; drawn ; 2 } ; club } ; abercynon rfc } }
select the rows whose champion record fuzzily matches to spartak moscow . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; champion ; spartak moscow } } ; 7 }
the average of the attendance record of all rows is 56169 .
Output: round_eq { avg { all_rows ; attendance } ; 56169 }
select the rows whose transmission record fuzzily matches to 8 - speed . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; transmission ; 8 - speed } } ; 6 }
for the date of vacancy records of all rows , most of them fuzzily match to 2010 .
Output: most_eq { all_rows ; date of vacancy ; 2010 }
select the rows whose time record is less than 22.2 . there is only one such row in the table . the athlete record of this unqiue row is veronica campbell - brown .
Output: and { only { filter_less { all_rows ; time ; 22.2 } } ; eq { hop { filter_less { all_rows ; time ; 22.2 } ; athlete } ; veronica campbell - brown } }
the average of the starts record of all rows is 7.67 .
Output: round_eq { avg { all_rows ; starts } ; 7.67 }
select the row whose episode number record of all rows is 1st minimum . the musical guest ( song performed ) record of this row is the courteeners ( no you did n't , no you do n't ) .
Output: eq { hop { nth_argmin { all_rows ; episode number ; 1 } ; musical guest ( song performed ) } ; the courteeners ( no you did n't , no you do n't ) }
select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; tv time ; fox } } ; 2 }
select the rows whose player record fuzzily matches to jim farmer . take the no record of this row . select the rows whose player record fuzzily matches to derek fisher . take the no record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; jim farmer } ; no } ; hop { filter_eq { all_rows ; player ; derek fisher } ; no } }
for the reason for non - seating records of all rows , most of them fuzzily match to died .
Output: most_eq { all_rows ; reason for non - seating ; died }
for the matches records of all rows , all of them are greater than or equal to 30 .
Output: all_greater_eq { all_rows ; matches ; 30 }
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: and { only { filter_eq { all_rows ; bodyweight ; 69.73 } } ; eq { hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name } ; nelly rivera ( dom ) } }
for the score records of all rows , most of them fuzzily match to w .
Output: most_eq { all_rows ; score ; w }
select the row whose goals scored ( gf ) record of all rows is maximum . the team ( equipo ) record of this row is arabe unido .
Output: eq { hop { argmax { all_rows ; goals scored ( gf ) } ; team ( equipo ) } ; arabe unido }
the sum of the runs record of all rows is 2617 .
Output: round_eq { sum { all_rows ; runs } ; 2617 }
select the row whose max speed ( mph ) record of all rows is 2nd maximum . the pilot record of this row is joseph a walker .
Output: eq { hop { nth_argmax { all_rows ; max speed ( mph ) ; 2 } ; pilot } ; joseph a walker }
select the row whose time record of all rows is 3rd minimum . the rowers record of this row is misaki kumakura , akiko iwamoto .
Output: eq { hop { nth_argmin { all_rows ; time ; 3 } ; rowers } ; misaki kumakura , akiko iwamoto }
select the row whose age at appointment record of all rows is maximum . the foreign nationality record of this row is canadian .
Output: eq { hop { argmax { all_rows ; age at appointment } ; foreign nationality } ; canadian }
select the rows whose largest ethnic group ( 2002 ) record fuzzily matches to serbs . for the type records of these rows , most of them fuzzily match to village .
Output: most_eq { filter_eq { all_rows ; largest ethnic group ( 2002 ) ; serbs } ; type ; village }
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: and { only { filter_eq { all_rows ; goals ; 23 } } ; eq { hop { filter_eq { all_rows ; goals ; 23 } ; player } ; mehmet yılmaz } }
the average of the season record of all rows is 2007 .
Output: round_eq { avg { all_rows ; season } ; 2007 }
select the rows whose competition record fuzzily matches to friendly . among these rows , select the rows whose venue record fuzzily matches to stadion villach lind , villach , austria . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; competition ; friendly } ; venue ; stadion villach lind , villach , austria } } ; 2 }
select the rows whose title record fuzzily matches to dengeki game appli . take the first published record of this row . select the rows whose title record fuzzily matches to rekidama . take the first published record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; dengeki game appli } ; first published } ; hop { filter_eq { all_rows ; title ; rekidama } ; first published } }
select the row whose release date record of all rows is 1st minimum . the developer ( s ) record of this row is valve corporation .
Output: eq { hop { nth_argmin { all_rows ; release date ; 1 } ; developer ( s ) } ; valve corporation }
select the rows whose 7:30 record fuzzily matches to jeopardy! . there is only one such row in the table .
Output: only { filter_eq { all_rows ; 7:30 ; jeopardy! } }
select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated . there is only one such row in the table . the name record of this unqiue row is st teresa 's school .
Output: and { only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school } }
select the rows whose position in 2001 record fuzzily matches to first league . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position in 2001 ; first league } } ; 2 }
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: and { only { filter_eq { all_rows ; scoring rank ; n/a } } ; eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 } }
the average of the time record of all rows is 6:43.99 .
Output: round_eq { avg { all_rows ; time } ; 6:43.99 }
select the row whose socialist record of all rows is maximum . the date released record of this row is august 4 , 1991 .
Output: eq { hop { argmax { all_rows ; socialist } ; date released } ; august 4 , 1991 }
select the rows whose high rebounds record fuzzily matches to garnett . the sum of the high rebounds record of these rows is 65 .
Output: round_eq { sum { filter_eq { all_rows ; high rebounds ; garnett } ; high rebounds } ; 65 }
select the rows whose runner - up record fuzzily matches to kanto gakuin university . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; runner - up ; kanto gakuin university } } ; 4 }
select the rows whose round record fuzzily matches to friendly . select the row whose spectators record of these rows is maximum . the date record of this row is 14 november 2012 .
Output: eq { hop { argmax { filter_eq { all_rows ; round ; friendly } ; spectators } ; date } ; 14 november 2012 }
select the rows whose name record fuzzily matches to darwin robinson . take the round record of this row . select the rows whose name record fuzzily matches to joe miller . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; darwin robinson } ; round } ; hop { filter_eq { all_rows ; name ; joe miller } ; round } }
the average of the capacity record of all rows is 9900 .
Output: round_eq { avg { all_rows ; capacity } ; 9900 }
for the score records of all rows , most of them are equal to 68 .
Output: most_eq { all_rows ; score ; 68 }
select the rows whose position record fuzzily matches to rb . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; rb } } ; 3 }
select the rows whose genre record fuzzily matches to sitcom . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; genre ; sitcom } } ; 2 }
for the city records of all rows , most of them fuzzily match to los angeles , california .
Output: most_eq { all_rows ; city ; los angeles , california }
select the rows whose location record does not match to ma . the number of such rows is 2 .
Output: eq { count { filter_not_eq { all_rows ; location ; ma } } ; 2 }
select the rows whose results record fuzzily matches to re - elected . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; results ; re - elected } } ; 7 }
select the rows whose call sign record fuzzily matches to cjjc - fm . take the frequency record of this row . select the rows whose call sign record fuzzily matches to cfgw - fm . take the frequency record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; call sign ; cjjc - fm } ; frequency } ; hop { filter_eq { all_rows ; call sign ; cfgw - fm } ; frequency } }
select the rows whose season record is equal to 2011 . the maximum races record of these rows is 18 .
Output: eq { max { filter_eq { all_rows ; season ; 2011 } ; races } ; 18 }
the sum of the races record of all rows is 198 .
Output: round_eq { sum { all_rows ; races } ; 198 }
select the rows whose competition record fuzzily matches to 1997 korea cup . among these rows , select the rows whose score record fuzzily matches to 1 goal . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; competition ; 1997 korea cup } ; score ; 1 goal } } ; 2 }
for the original us air date records of all rows , all of them fuzzily match to 2012 .
Output: all_eq { all_rows ; original us air date ; 2012 }
the sum of the crowd record of all rows is 134,131 .
Output: round_eq { sum { all_rows ; crowd } ; 134,131 }
select the rows whose high school record fuzzily matches to lincoln . take the enrollment record of this row . select the rows whose high school record fuzzily matches to tacoma school of the arts . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; high school ; lincoln } ; enrollment } ; hop { filter_eq { all_rows ; high school ; tacoma school of the arts } ; enrollment } }
the average of the number of seasons in top division record of all rows is 16 .
Output: round_eq { avg { all_rows ; number of seasons in top division } ; 16 }
select the rows whose circuit record fuzzily matches to marcus theatres . take the sites record of this row . select the rows whose circuit record fuzzily matches to rave motion pictures . take the sites record of this row . the second record is 7 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; circuit ; marcus theatres } ; sites } ; hop { filter_eq { all_rows ; circuit ; rave motion pictures } ; sites } } ; -7 }
select the rows whose high assists record fuzzily matches to kevin garnett . there is only one such row in the table . the date record of this unqiue row is december 21 . the team record of this unqiue row is new york .
Output: and { only { filter_eq { all_rows ; high assists ; kevin garnett } } ; and { eq { hop { filter_eq { all_rows ; high assists ; kevin garnett } ; date } ; december 21 } ; eq { hop { filter_eq { all_rows ; high assists ; kevin garnett } ; team } ; new york } } }
select the row whose frequency mhz record of all rows is 2nd minimum . the city of license record of this row is lyons falls , ny .
Output: eq { hop { nth_argmin { all_rows ; frequency mhz ; 2 } ; city of license } ; lyons falls , ny }
select the rows whose high points record fuzzily matches to dell curry . there is only one such row in the table . the date record of this unqiue row is may 2 .
Output: and { only { filter_eq { all_rows ; high points ; dell curry } } ; eq { hop { filter_eq { all_rows ; high points ; dell curry } ; date } ; may 2 } }
the maximum points record of all rows is 581 . the player record of the row with superlative points record is kendall gill .
Output: and { eq { max { all_rows ; points } ; 581 } ; eq { hop { argmax { all_rows ; points } ; player } ; kendall gill } }
select the rows whose standard record fuzzily matches to gsm . for the frequency records of these rows , all of them fuzzily match to 900 mhz and 1800 mhz .
Output: all_eq { filter_eq { all_rows ; standard ; gsm } ; frequency ; 900 mhz and 1800 mhz }
select the row whose date record of all rows is 2nd minimum . the event record of this row is 2008 european poker championships .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; event } ; 2008 european poker championships }
select the row whose position record of all rows is minimum . the season record of this row is 2006 .
Output: eq { hop { argmin { all_rows ; position } ; season } ; 2006 }
for the competition records of all rows , most of them fuzzily match to uefa intertoto cup .
Output: most_eq { all_rows ; competition ; uefa intertoto cup }
select the rows whose air date record fuzzily matches to january . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; air date ; january } } ; 4 }
select the rows whose year built record is less than 1960 . the number of such rows is 4 .
Output: eq { count { filter_less { all_rows ; year built ; 1960 } } ; 4 }
select the row whose date record of all rows is minimum . the location attendance record of this row is united center 23854 .
Output: eq { hop { argmin { all_rows ; date } ; location attendance } ; united center 23854 }
select the rows whose call sign record fuzzily matches to w245ac . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k216fw . take the frequency mhz record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; call sign ; w245ac } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k216fw } ; frequency mhz } }
select the row whose founded record of all rows is 3rd minimum . the institution record of this row is university of california , davis .
Output: eq { hop { nth_argmin { all_rows ; founded ; 3 } ; institution } ; university of california , davis }
select the rows whose constituency record fuzzily matches to nordland . take the electorate record of this row . select the rows whose constituency record fuzzily matches to troms . take the electorate record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; constituency ; nordland } ; electorate } ; hop { filter_eq { all_rows ; constituency ; troms } ; electorate } }
the 2nd maximum money record of all rows is 135000 . the player record of the row with 2nd maximum money record is fred couples .
Output: and { eq { nth_max { all_rows ; money ; 2 } ; 135000 } ; eq { hop { nth_argmax { all_rows ; money ; 2 } ; player } ; fred couples } }
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: eq { diff { hop { filter_eq { all_rows ; date ; nov 15 } ; cowboys points } ; hop { filter_eq { all_rows ; date ; nov 8 } ; cowboys points } } ; 2 }
for the laps records of all rows , most of them are greater than 60 .
Output: most_greater { all_rows ; laps ; 60 }
select the rows whose player record is arbitrary . the number of such rows is 9 .
Output: eq { count { filter_all { all_rows ; player } } ; 9 }
select the rows whose position record fuzzily matches to 2nd . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; position ; 2nd } } ; 4 }
select the rows whose tournament record fuzzily matches to indianapolis . take the date record of this row . select the rows whose tournament record fuzzily matches to palm beach gardens . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; indianapolis } ; date } ; hop { filter_eq { all_rows ; tournament ; palm beach gardens } ; date } }
select the row whose attendance record of all rows is 3rd maximum . the game record of this row is 1 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; game } ; 1 }
the average of the gold record of all rows is 0.86 .
Output: round_eq { avg { all_rows ; gold } ; 0.86 }
select the row whose edges record of all rows is maximum . the dual archimedean solid record of this row is truncated icosidodecahedron .
Output: eq { hop { argmax { all_rows ; edges } ; dual archimedean solid } ; truncated icosidodecahedron }
select the rows whose gold record is equal to 0 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; gold ; 0 } } ; 4 }
the 2nd minimum year record of all rows is 1974 . the partner record of the row with 2nd minimum year record is roscoe tanner .
Output: and { eq { nth_min { all_rows ; year ; 2 } ; 1974 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; partner } ; roscoe tanner } }
select the rows whose home record fuzzily matches to tampa bay . among these rows , select the rows whose attendance record is greater than 19,000 . the number of such rows is 4 .
Output: eq { count { filter_greater { filter_eq { all_rows ; home ; tampa bay } ; attendance ; 19,000 } } ; 4 }
the average of the points record of all rows is 58 .
Output: round_eq { avg { all_rows ; points } ; 58 }
select the row whose erp w record of all rows is maximum . the call sign record of this row is k211ch .
Output: eq { hop { argmax { all_rows ; erp w } ; call sign } ; k211ch }
select the rows whose agg record fuzzily matches to 0 . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; agg ; 0 } } ; 1 }
for the winning amount records of all rows , most of them fuzzily match to rs 10 , 00000 .
Output: most_eq { all_rows ; winning amount ; rs 10 , 00000 }
select the rows whose crowd record is less than 10000 . the average of the crowd record of these rows is 7005 .
Output: round_eq { avg { filter_less { all_rows ; crowd ; 10000 } ; crowd } ; 7005 }
for the competition records of all rows , most of them fuzzily match to 2006 afc challenge cup .
Output: most_eq { all_rows ; competition ; 2006 afc challenge cup }
select the row whose 1 euro = record of all rows is 2nd maximum . the currency record of this row is colombian peso ( cop ) .
Output: eq { hop { nth_argmax { all_rows ; 1 euro
for the duration records of all rows , most of them are greater than 4:00 .
Output: most_greater { all_rows ; duration ; 4:00 }
select the rows whose men 's singles record fuzzily matches to tan joe hok . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; men 's singles ; tan joe hok } } ; 2 }
select the rows whose written by record fuzzily matches to diana sproveri . there is only one such row in the table . the title record of this unqiue row is my boss ate my homework .
Output: and { only { filter_eq { all_rows ; written by ; diana sproveri } } ; eq { hop { filter_eq { all_rows ; written by ; diana sproveri } ; title } ; my boss ate my homework } }
the sum of the points jury record of all rows is 235 .
Output: round_eq { sum { all_rows ; points jury } ; 235 }
select the rows whose record label record fuzzily matches to wild world . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; record label ; wild world } } ; 3 }
the sum of the enrollment record of all rows is 11100 .
Output: round_eq { sum { all_rows ; enrollment } ; 11100 }