niryuu/sni-each-converted huggingface dataset:
all_greater { filter_eq { all_rows ; gpu frequency ; 320 mhz } ; release price ( usd ) ; 60 } = true
Output: select the rows whose gpu frequency record fuzzily matches to 320 mhz . for the release price  usd  records of these rows , all of them are greater than 60 .
most_eq { all_rows ; laid down ; february 1935 } = true
Output: for the laid down records of all rows , most of them fuzzily match to february 1935 .
eq { count { filter_eq { all_rows ; school colors ; orange & black } } ; 2 } = true
Output: select the rows whose school colors record fuzzily matches to orange  black . the number of such rows is 2 .
round_eq { avg { all_rows ; crowd } ; 21012 } = true
Output: the average of the crowd record of all rows is 21012 .
all_eq { all_rows ; team ; honda } = true
Output: for the team records of all rows , all of them fuzzily match to honda .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; punt road oval } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is punt road oval .
round_eq { avg { all_rows ; average } ; 25.2 } = true
Output: the average of the average record of all rows is 25.2 .
eq { hop { nth_argmin { filter_eq { all_rows ; score ; ot } ; january ; 3 } ; opponent } ; st louis blues } = true
Output: select the rows whose score record fuzzily matches to ot . select the row whose january record of these rows is 3rd minimum . the opponent record of this row is st louis blues .
eq { count { filter_greater { filter_eq { all_rows ; location ; st pete times forum } ; attendance ; 16000 } } ; 5 } = true
Output: select the rows whose location record fuzzily matches to st pete times forum . among these rows , select the rows whose attendance record is greater than 16000 . the number of such rows is 5 .
all_eq { all_rows ; sat 29 may ; cancelled no time } = true
Output: for the sat 29 may records of all rows , all of them fuzzily match to cancelled no time .
eq { count { filter_less_eq { filter_eq { all_rows ; arena ( capacity ) ; 5000 } ; previous season ; 5 } } ; 2 } = true
Output: select the rows whose arena  capacity  record fuzzily matches to 5000 . among these rows , select the rows whose previous season record is less than or equal to 5 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; character ; mac taylor csi detective } ; episodes } ; hop { filter_eq { all_rows ; character ; jo danville csi detective } ; episodes } } = true
Output: select the rows whose character record fuzzily matches to mac taylor csi detective . take the episodes record of this row . select the rows whose character record fuzzily matches to jo danville csi detective . take the episodes record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; times contested } ; 2.17 } = true
Output: the average of the times contested record of all rows is 2.17 .
only { filter_eq { filter_eq { all_rows ; city / state ; phillip island , victoria } ; team ; volvo racing } } = true
Output: select the rows whose city  state record fuzzily matches to phillip island , victoria . among these rows , select the rows whose team record fuzzily matches to volvo racing . there is only one such row in the table .
round_eq { avg { all_rows ; capacity } ; 15684 } = true
Output: the average of the capacity record of all rows is 15684 .
and { only { filter_greater { all_rows ; gold ; 100 } } ; eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok } } = true
Output: select the rows whose gold record is greater than 100 . there is only one such row in the table . the province record of this unqiue row is bangkok .
eq { diff { hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; western oval } ; crowd } } ; 8099 } = true
Output: select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . the first record is 8099 larger than the second record .
eq { hop { argmin { filter_eq { all_rows ; position ; ol } ; pick } ; cfl team } ; montreal alouettes } = true
Output: select the rows whose position record fuzzily matches to ol . select the row whose pick record of these rows is minimum . the cfl team record of this row is montreal alouettes .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; princes park } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is princes park .
eq { count { filter_not_eq { all_rows ; visitor ; philadelphia } } ; 7 } = true
Output: select the rows whose visitor record does not match to philadelphia . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; to par ; - 9 } } ; 4 } = true
Output: select the rows whose to par record fuzzily matches to  9 . the number of such rows is 4 .
round_eq { avg { all_rows ; first elected } ; 1992 } = true
Output: the average of the first elected record of all rows is 1992 .
greater { hop { filter_eq { all_rows ; season ; 2010 } ; podium } ; hop { filter_eq { all_rows ; season ; 2011 } ; podium } } = true
Output: select the rows whose season record fuzzily matches to 2010 . take the podium record of this row . select the rows whose season record fuzzily matches to 2011 . take the podium record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; age at appointment ; 50 } } ; 4 } = true
Output: select the rows whose age at appointment record is equal to 50 . the number of such rows is 4 .
eq { count { filter_greater { filter_eq { all_rows ; street address ; north franklin street } ; height ft ( m ) ; 200 } } ; 2 } = true
Output: select the rows whose street address record fuzzily matches to north franklin street . among these rows , select the rows whose height ft  m  record is greater than 200 . the number of such rows is 2 .
round_eq { avg { all_rows ; attendance } ; 58252 } = true
Output: the average of the attendance record of all rows is 58252 .
eq { hop { argmax { all_rows ; attendance } ; week } ; 6 } = true
Output: select the row whose attendance record of all rows is maximum . the week record of this row is 6 .
round_eq { avg { all_rows ; score } ; 34.5 } = true
Output: the average of the score record of all rows is 34.5 .
eq { count { filter_eq { all_rows ; written by ; adam barr } } ; 3 } = true
Output: select the rows whose written by record fuzzily matches to adam barr . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; premiere ; 14 august } } ; 4 } = true
Output: select the rows whose premiere record fuzzily matches to 14 august . the number of such rows is 4 .
eq { max { all_rows ; points } ; 141 } = true
Output: the maximum points record of all rows is 141 .
and { only { filter_eq { all_rows ; writer ( s ) ; mark james } } ; and { eq { hop { filter_eq { all_rows ; writer ( s ) ; mark james } ; track } ; 4 } ; eq { hop { filter_eq { all_rows ; writer ( s ) ; mark james } ; song title } ; moody blue } } } = true
Output: select the rows whose writer  s  record fuzzily matches to mark james . there is only one such row in the table . the track record of this unqiue row is 4 . the song title record of this unqiue row is moody blue .
and { eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; footscray } ; eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; date } ; 20 june 1942 } } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is footscray . the date record of this row is 20 june 1942 .
eq { hop { nth_argmax { all_rows ; lost ( pp ) ; 2 } ; team ( equipo ) } ; plaza amador } = true
Output: select the row whose lost  pp  record of all rows is 2nd maximum . the team  equipo  record of this row is plaza amador .
eq { min { all_rows ; date } ; 2 feb 2008 } = true
Output: the minimum date record of all rows is 2 feb 2008 .
greater { hop { filter_eq { all_rows ; celebrity ; paweł stasiak } ; average } ; hop { filter_eq { all_rows ; celebrity ; paolo cozza } ; average } } = true
Output: select the rows whose celebrity record fuzzily matches to paweł stasiak . take the average record of this row . select the rows whose celebrity record fuzzily matches to paolo cozza . take the average record of this row . the first record is greater than the second record .
most_eq { all_rows ; nationality ; rome } = true
Output: for the nationality records of all rows , most of them fuzzily match to rome .
eq { hop { argmax { all_rows ; gross } ; title } ; et the extra - terrestrial } = true
Output: select the row whose gross record of all rows is maximum . the title record of this row is et the extra  terrestrial .
eq { count { filter_eq { filter_eq { all_rows ; high assists ; steve nash } ; location attendance ; us airways center } } ; 6 } = true
Output: select the rows whose high assists record fuzzily matches to steve nash . among these rows , select the rows whose location attendance record fuzzily matches to us airways center . the number of such rows is 6 .
most_eq { all_rows ; type ; streetcar } = true
Output: for the type records of all rows , most of them fuzzily match to streetcar .
eq { count { filter_all { all_rows ; player } } ; 8 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 8 .
eq { hop { argmin { filter_eq { all_rows ; position ; lb } ; overall } ; name } ; cody glenn } = true
Output: select the rows whose position record fuzzily matches to lb . select the row whose overall record of these rows is minimum . the name record of this row is cody glenn .
round_eq { sum { all_rows ; against } ; 53 } = true
Output: the sum of the against record of all rows is 53 .
and { only { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } } ; eq { hop { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } ; incumbent } ; harold ford jr } } = true
Output: select the rows whose results record fuzzily matches to retired to run for us senate democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is harold ford jr .
all_eq { all_rows ; competition ; friendly } = true
Output: for the competition records of all rows , all of them fuzzily match to friendly .
round_eq { avg { all_rows ; socialist } ; 45 % } = true
Output: the average of the socialist record of all rows is 45  .
all_eq { all_rows ; date ; 05 } = true
Output: for the date records of all rows , all of them fuzzily match to 05 .
and { only { filter_eq { all_rows ; opponents ; ashleigh barty casey dellacqua } } ; and { eq { hop { filter_eq { all_rows ; opponents ; ashleigh barty casey dellacqua } ; year } ; 2013 } ; eq { hop { filter_eq { all_rows ; opponents ; ashleigh barty casey dellacqua } ; championship } ; australian open } } } = true
Output: select the rows whose opponents record fuzzily matches to ashleigh barty casey dellacqua . there is only one such row in the table . the year record of this unqiue row is 2013 . the championship record of this unqiue row is australian open .
and { only { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } } ; eq { hop { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } ; genus / species } ; bartonella tribocorum } } = true
Output: select the rows whose gene name record fuzzily matches to alanyl  trna synthetase . there is only one such row in the table . the genus  species record of this unqiue row is bartonella tribocorum .
all_eq { all_rows ; away captain ; joe darling } = true
Output: for the away captain records of all rows , all of them fuzzily match to joe darling .
round_eq { avg { all_rows ; south asians 2001 } ; 70544.6 } = true
Output: the average of the south asians 2001 record of all rows is 70544.6 .
less { hop { filter_eq { all_rows ; call sign ; wjds } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w298as } ; frequency mhz } } = true
Output: select the rows whose call sign record fuzzily matches to wjds . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to w298as . take the frequency mhz record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; player ; adam gilchrist } ; matches } ; hop { filter_eq { all_rows ; player ; steve rixon } ; matches } } = true
Output: select the rows whose player record fuzzily matches to adam gilchrist . take the matches record of this row . select the rows whose player record fuzzily matches to steve rixon . take the matches record of this row . the first record is greater than the second record .
most_eq { all_rows ; high points ; brandon roy } = true
Output: for the high points records of all rows , most of them fuzzily match to brandon roy .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; mcg } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is mcg .
eq { hop { argmin { all_rows ; date } ; venue } ; mcg } = true
Output: select the row whose date record of all rows is minimum . the venue record of this row is mcg .
eq { count { filter_eq { filter_less_eq { all_rows ; pick ; 5 } ; country of origin ; philippines } } ; 4 } = true
Output: select the rows whose pick record is less than or equal to 5 . among these rows , select the rows whose country of origin record fuzzily matches to philippines . the number of such rows is 4 .
only { filter_eq { filter_eq { all_rows ; date ; oct } ; opponent ; new england patriots } } = true
Output: select the rows whose date record fuzzily matches to oct . among these rows , select the rows whose opponent record fuzzily matches to new england patriots . there is only one such row in the table .
eq { count { filter_greater { filter_greater { all_rows ; crowd ; 20000 } ; home team score ; 10 } } ; 2 } = true
Output: select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 .
eq { nth_min { filter_eq { all_rows ; winner ; learco guerra } ; date ; 2 } ; 18 may } = true
Output: select the rows whose winner record fuzzily matches to learco guerra . the 2nd minimum date record of these rows is 18 may .
round_eq { avg { all_rows ; age } ; 25.6 } = true
Output: the average of the age record of all rows is 25.6 .
eq { count { filter_eq { all_rows ; elevated ; 1117 } } ; 3 } = true
Output: select the rows whose elevated record fuzzily matches to 1117 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; first elected ; 1878 } } ; 3 } = true
Output: select the rows whose first elected record fuzzily matches to 1878 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; nationality ; spain } } ; eq { hop { filter_eq { all_rows ; nationality ; spain } ; player } ; raúl lópez } } = true
Output: select the rows whose nationality record fuzzily matches to spain . there is only one such row in the table . the player record of this unqiue row is raúl lópez .
and { only { filter_eq { all_rows ; constellation ; hydra } } ; eq { hop { filter_eq { all_rows ; constellation ; hydra } ; ngc number } ; 5078 } } = true
Output: select the rows whose constellation record fuzzily matches to hydra . there is only one such row in the table . the ngc number record of this unqiue row is 5078 .
round_eq { avg { all_rows ; power } ; 8260 } = true
Output: the average of the power record of all rows is 8260 .
eq { count { filter_eq { all_rows ; womens singles ; aya umemura } } ; 2 } = true
Output: select the rows whose womens singles record fuzzily matches to aya umemura . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; directedby ; guy ferland } } ; 2 } = true
Output: select the rows whose directedby record fuzzily matches to guy ferland . the number of such rows is 2 .
most_eq { all_rows ; released ; 2012 } = true
Output: for the released records of all rows , most of them fuzzily match to 2012 .
eq { count { filter_eq { all_rows ; lec sport ; tennis } } ; 2 } = true
Output: select the rows whose lec sport record fuzzily matches to tennis . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; ralph a gamble } = true
Output: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is ralph a gamble .
greater { hop { filter_eq { all_rows ; model ; amd 800 chipset series } ; tdp ( w ) } ; hop { filter_eq { all_rows ; model ; amd 700 chipset series } ; tdp ( w ) } } = true
Output: select the rows whose model record fuzzily matches to amd 800 chipset series . take the tdp  w  record of this row . select the rows whose model record fuzzily matches to amd 700 chipset series . take the tdp  w  record of this row . the first record is greater than the second record .
and { eq { max { all_rows ; 2010 - list } ; 27 } ; eq { hop { argmax { all_rows ; 2010 - list } ; country } ; sweden } } = true
Output: the maximum 2010  list record of all rows is 27 . the country record of the row with superlative 2010  list record is sweden .
round_eq { avg { all_rows ; attendance } ; 56346 } = true
Output: the average of the attendance record of all rows is 56346 .
and { only { filter_greater { all_rows ; time ; 48 } } ; eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida } } = true
Output: select the rows whose time record is greater than 48 . there is only one such row in the table . the athlete record of this unqiue row is naiel santiago dalmeida .
round_eq { avg { filter_eq { all_rows ; event ; 50 m } ; time } ; 24.41 } = true
Output: select the rows whose event record fuzzily matches to 50 m . the average of the time record of these rows is 24.41 .
and { only { filter_eq { all_rows ; score ; 0 - } } ; eq { hop { filter_eq { all_rows ; score ; 0 - } ; game } ; 30 } } = true
Output: select the rows whose score record fuzzily matches to 0  . there is only one such row in the table . the game record of this unqiue row is 30 .
eq { count { filter_eq { all_rows ; date ; 2000 } } ; 16 } = true
Output: select the rows whose date record fuzzily matches to 2000 . the number of such rows is 16 .
eq { hop { nth_argmax { all_rows ; height ; 3 } ; player } ; caner pekşen } = true
Output: select the row whose height record of all rows is 3rd maximum . the player record of this row is caner pekşen .
round_eq { sum { all_rows ; round } ; 6 } = true
Output: the sum of the round record of all rows is 6 .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; march 4 } = true
Output: select the row whose attendance record of all rows is 1st maximum . the date record of this row is march 4 .
all_eq { all_rows ; work ; the secret life of the american teenager } = true
Output: for the work records of all rows , all of them fuzzily match to the secret life of the american teenager .
less { hop { filter_eq { all_rows ; player ; bob tway } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; phil mickelson } ; year ( s ) won } } = true
Output: select the rows whose player record fuzzily matches to bob tway . take the year  s  won record of this row . select the rows whose player record fuzzily matches to phil mickelson . take the year  s  won record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; championships in st louis } ; team } ; st louis stars } = true
Output: select the row whose championships in st louis record of all rows is maximum . the team record of this row is st louis stars .
eq { max { all_rows ; attendance } ; charlotte coliseum 23698 } = true
Output: the maximum attendance record of all rows is charlotte coliseum 23698 .
eq { count { filter_eq { all_rows ; lost ; 0 } } ; 5 } = true
Output: select the rows whose lost record is equal to 0 . the number of such rows is 5 .
and { only { filter_greater { all_rows ; gold ; 10 } } ; eq { hop { filter_greater { all_rows ; gold ; 10 } ; nation } ; united states } } = true
Output: select the rows whose gold record is greater than 10 . there is only one such row in the table . the nation record of this unqiue row is united states .
and { eq { max { all_rows ; away team score } ; 22.19 ( 151 ) } ; eq { hop { argmax { all_rows ; away team score } ; date } ; 1 june 1935 } } = true
Output: the maximum away team score record of all rows is 22.19  151  . the date record of the row with superlative away team score record is 1 june 1935 .
eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance } ; 47475 } = true
Output: select the rows whose opponent record fuzzily matches to chicago bears . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 47475 .
eq { hop { argmax { all_rows ; crowd } ; venue } ; junction oval } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is junction oval .
eq { count { filter_eq { all_rows ; position ; left wing } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to left wing . the number of such rows is 3 .
round_eq { avg { all_rows ; goals } ; 3.9 } = true
Output: the average of the goals record of all rows is 3.9 .
greater { hop { filter_eq { all_rows ; opponent ; queensland } ; runs } ; hop { filter_eq { all_rows ; opponent ; victoria } ; runs } } = true
Output: select the rows whose opponent record fuzzily matches to queensland . take the runs record of this row . select the rows whose opponent record fuzzily matches to victoria . take the runs record of this row . the first record is greater than the second record .
eq { min { filter_eq { all_rows ; category ; best series - drama } ; year } ; 1994 } = true
Output: select the rows whose category record fuzzily matches to best series  drama . the minimum year record of these rows is 1994 .
eq { hop { nth_argmin { all_rows ; react ; 5 } ; name } ; christopher williams } = true
Output: select the row whose react record of all rows is 5th minimum . the name record of this row is christopher williams .
and { eq { nth_min { all_rows ; rank ; 2 } ; 2nd } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; population } ; 36039 } } = true
Output: the 2nd minimum rank record of all rows is 2nd . the population record of the row with 2nd minimum rank record is 36039 .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; opponents } ; 1 . fc heidenheim } = true
Output: select the row whose date record of all rows is 2nd minimum . the opponents record of this row is 1 . fc heidenheim .
all_eq { filter_eq { all_rows ; first elected ; 19 } ; party ; democratic } = true
Output: select the rows whose first elected record fuzzily matches to 19 . for the party records of these rows , all of them fuzzily match to democratic .
greater { hop { filter_eq { all_rows ; school ( ihsaa id ) ; knox community } ; enrollment } ; hop { filter_eq { all_rows ; school ( ihsaa id ) ; culver community } ; enrollment } } = true
Output: select the rows whose school  ihsaa id  record fuzzily matches to knox community . take the enrollment record of this row . select the rows whose school  ihsaa id  record fuzzily matches to culver community . take the enrollment record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; population density ( / km 2 ) ; 2 } ; main town } ; chilawathurai } = true
Output: select the row whose population density   km 2  record of all rows is 2nd minimum . the main town record of this row is chilawathurai .
eq { count { filter_greater { all_rows ; floors ; 30 } } ; 3 } = true
Output: select the rows whose floors record is greater than 30 . the number of such rows is 3 .
most_eq { filter_eq { all_rows ; country ; united states } ; year ( s ) won ; 198 } = true
Output: select the rows whose country record fuzzily matches to united states . for the year  s  won records of these rows , most of them fuzzily match to 198 .
greater { hop { filter_eq { all_rows ; song ; shady lady } ; points } ; hop { filter_eq { all_rows ; song ; peace will come } ; points } } = true
Output: select the rows whose song record fuzzily matches to shady lady . take the points record of this row . select the rows whose song record fuzzily matches to peace will come . take the points record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; points } ; 66 } = true
Output: the sum of the points record of all rows is 66 .
most_eq { all_rows ; surface ; carpet ( i ) } = true
Output: for the surface records of all rows , most of them fuzzily match to carpet  i  .
and { only { filter_eq { all_rows ; status ; millennium trophy match } } ; eq { hop { filter_eq { all_rows ; status ; millennium trophy match } ; opposing teams } ; ireland } } = true
Output: select the rows whose status record fuzzily matches to millennium trophy match . there is only one such row in the table . the opposing teams record of this unqiue row is ireland .
less { hop { filter_eq { all_rows ; title ; college bound } ; original air date } ; hop { filter_eq { all_rows ; title ; the cadet } ; original air date } } = true
Output: select the rows whose title record fuzzily matches to college bound . take the original air date record of this row . select the rows whose title record fuzzily matches to the cadet . take the original air date record of this row . the first record is less than the second record .
eq { count { filter_eq { filter_eq { all_rows ; country ; china } ; industry ; petroleum } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to china . among these rows , select the rows whose industry record fuzzily matches to petroleum . the number of such rows is 2 .
eq { hop { argmin { all_rows ; time } ; country } ; belarus } = true
Output: select the row whose time record of all rows is minimum . the country record of this row is belarus .
and { only { filter_eq { all_rows ; partnering ; neuza silva } } ; eq { hop { filter_eq { all_rows ; partnering ; neuza silva } ; edition } ; 2010 fed cup europe / africa group i } } = true
Output: select the rows whose partnering record fuzzily matches to neuza silva . there is only one such row in the table . the edition record of this unqiue row is 2010 fed cup europe  africa group i .
eq { count { filter_eq { all_rows ; venue ; away } } ; 4 } = true
Output: select the rows whose venue record fuzzily matches to away . the number of such rows is 4 .
round_eq { sum { all_rows ; screens } ; 461 } = true
Output: the sum of the screens record of all rows is 461 .
eq { hop { nth_argmin { all_rows ; released ; 2 } ; title } ; mission to magnus } = true
Output: select the row whose released record of all rows is 2nd minimum . the title record of this row is mission to magnus .
all_eq { all_rows ; date ; 6 june 1970 } = true
Output: for the date records of all rows , all of them fuzzily match to 6 june 1970 .
all_eq { all_rows ; region ; japan } = true
Output: for the region records of all rows , all of them fuzzily match to japan .
less { hop { filter_eq { all_rows ; name ; erna hanfstaengl } ; life } ; hop { filter_eq { all_rows ; name ; maria reiter } ; life } } = true
Output: select the rows whose name record fuzzily matches to erna hanfstaengl . take the life record of this row . select the rows whose name record fuzzily matches to maria reiter . take the life record of this row . the first record is less than the second record .
most_eq { all_rows ; drivetrain ; rwd } = true
Output: for the drivetrain records of all rows , most of them fuzzily match to rwd .
eq { max { filter_eq { all_rows ; constructor ; ferrari } ; date } ; 18 july } = true
Output: select the rows whose constructor record fuzzily matches to ferrari . the maximum date record of these rows is 18 july .
and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; tournament } ; valencia , spain } } = true
Output: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the tournament record of this unqiue row is valencia , spain .
round_eq { avg { all_rows ; enrollment } ; 1133 } = true
Output: the average of the enrollment record of all rows is 1133 .
most_eq { all_rows ; extra ; 200 m } = true
Output: for the extra records of all rows , most of them fuzzily match to 200 m .
and { only { filter_greater { all_rows ; winnings ; 300000 } } ; eq { hop { filter_greater { all_rows ; winnings ; 300000 } ; year } ; 1998 } } = true
Output: select the rows whose winnings record is greater than 300000 . there is only one such row in the table . the year record of this unqiue row is 1998 .
eq { count { filter_eq { all_rows ; chassis - engine ; porsche 962 c } } ; 8 } = true
Output: select the rows whose chassis  engine record fuzzily matches to porsche 962 c . the number of such rows is 8 .
eq { hop { argmin { filter_eq { all_rows ; suburb / town ; vincentia } ; founded } ; school } ; vincentia public school } = true
Output: select the rows whose suburb  town record fuzzily matches to vincentia . select the row whose founded record of these rows is minimum . the school record of this row is vincentia public school .
eq { hop { argmax { all_rows ; administrative panel } ; party } ; fine gael } = true
Output: select the row whose administrative panel record of all rows is maximum . the party record of this row is fine gael .
all_eq { all_rows ; label ; linfair records } = true
Output: for the label records of all rows , all of them fuzzily match to linfair records .
eq { diff { hop { filter_eq { all_rows ; school ; high point university } ; founded } ; hop { filter_eq { all_rows ; school ; coastal carolina university } ; founded } } ; -30 } = true
Output: select the rows whose school record fuzzily matches to high point university . take the founded record of this row . select the rows whose school record fuzzily matches to coastal carolina university . take the founded record of this row . the second record is 30 larger than the first record .
eq { count { filter_eq { filter_eq { all_rows ; country ; nigeria } ; country ; nigeria } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to nigeria . among these rows , select the rows whose country record fuzzily matches to nigeria . the number of such rows is 2 .
round_eq { avg { all_rows ; us viewers ( in millions ) } ; 9.15 } = true
Output: the average of the us viewers  in millions  record of all rows is 9.15 .
eq { hop { argmax { all_rows ; points } ; player } ; paul magoffin } = true
Output: select the row whose points record of all rows is maximum . the player record of this row is paul magoffin .
round_eq { sum { all_rows ; solo } ; 153 } = true
Output: the sum of the solo record of all rows is 153 .
eq { hop { argmin { all_rows ; position } ; competition } ; universiade } = true
Output: select the row whose position record of all rows is minimum . the competition record of this row is universiade .
eq { hop { argmin { all_rows ; place } ; player } ; hale irwin } = true
Output: select the row whose place record of all rows is minimum . the player record of this row is hale irwin .
all_eq { all_rows ; date ; july } = true
Output: for the date records of all rows , all of them fuzzily match to july .
eq { count { filter_eq { all_rows ; tournament ; world cross country championships } } ; 3 } = true
Output: select the rows whose tournament record fuzzily matches to world cross country championships . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; nation ; sweden } ; silver } ; hop { filter_eq { all_rows ; nation ; denmark } ; silver } } = true
Output: select the rows whose nation record fuzzily matches to sweden . take the silver record of this row . select the rows whose nation record fuzzily matches to denmark . take the silver record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; year ; 1967 } ; pts } ; hop { filter_eq { all_rows ; year ; 1965 } ; pts } } = true
Output: select the rows whose year record fuzzily matches to 1967 . take the pts record of this row . select the rows whose year record fuzzily matches to 1965 . take the pts record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; frank w boykin } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is frank w boykin .
round_eq { avg { all_rows ; attendance } ; 24652 } = true
Output: the average of the attendance record of all rows is 24652 .
round_eq { sum { filter_eq { all_rows ; year ; 1954 } ; points } ; 4 } = true
Output: select the rows whose year record is equal to 1954 . the sum of the points record of these rows is 4 .
eq { count { filter_eq { all_rows ; week ; hollywood } } ; 3 } = true
Output: select the rows whose week record fuzzily matches to hollywood . the number of such rows is 3 .
eq { diff { hop { filter_eq { all_rows ; incumbent ; wilbur mills } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james william trimble } ; first elected } } ; -6 years } = true
Output: select the rows whose incumbent record fuzzily matches to wilbur mills . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james william trimble . take the first elected record of this row . the second record is 6 years larger than the first record .
most_less { all_rows ; away team score ; 70 } = true
Output: for the away team score records of all rows , most of them are less than 70 .
greater { hop { filter_eq { all_rows ; player ; brandon bass } ; years in orlando } ; hop { filter_eq { all_rows ; player ; andre barrett } ; years in orlando } } = true
Output: select the rows whose player record fuzzily matches to brandon bass . take the years in orlando record of this row . select the rows whose player record fuzzily matches to andre barrett . take the years in orlando record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; school ; emmanuel christian school } ; size } ; hop { filter_eq { all_rows ; school ; wabash high school } ; size } } = true
Output: select the rows whose school record fuzzily matches to emmanuel christian school . take the size record of this row . select the rows whose school record fuzzily matches to wabash high school . take the size record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; position ; fb } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to fb . the number of such rows is 3 .
and { only { filter_eq { all_rows ; type ; military } } ; eq { hop { filter_eq { all_rows ; type ; military } ; institution } ; norwich university } } = true
Output: select the rows whose type record fuzzily matches to military . there is only one such row in the table . the institution record of this unqiue row is norwich university .
eq { count { filter_eq { all_rows ; reason for termination ; death } } ; 7 } = true
Output: select the rows whose reason for termination record fuzzily matches to death . the number of such rows is 7 .
greater { hop { filter_eq { all_rows ; title ; road to the north pole } ; original air date } ; hop { filter_eq { all_rows ; title ; road to europe } ; original air date } } = true
Output: select the rows whose title record fuzzily matches to road to the north pole . take the original air date record of this row . select the rows whose title record fuzzily matches to road to europe . take the original air date record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; country ; united states } } ; 7 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 7 .
eq { max { all_rows ; away } ; 2 - 0 } = true
Output: the maximum away record of all rows is 2  0 .
most_eq { all_rows ; notes ; short film } = true
Output: for the notes records of all rows , most of them fuzzily match to short film .
eq { count { filter_eq { all_rows ; school / club team ; creighton } } ; 2 } = true
Output: select the rows whose school  club team record fuzzily matches to creighton . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; series ; gp2 series } } ; 2 } = true
Output: select the rows whose series record fuzzily matches to gp2 series . the number of such rows is 2 .
most_eq { all_rows ; service ; freight } = true
Output: for the service records of all rows , most of them fuzzily match to freight .
and { only { filter_greater { all_rows ; latitude ; 0 } } ; eq { hop { filter_greater { all_rows ; latitude ; 0 } ; name } ; xi wang } } = true
Output: select the rows whose latitude record is greater than 0 . there is only one such row in the table . the name record of this unqiue row is xi wang .
less { hop { filter_eq { all_rows ; club ; biu chun rangers } ; position in 2012 - 13 } ; hop { filter_eq { all_rows ; club ; citizen } ; position in 2012 - 13 } } = true
Output: select the rows whose club record fuzzily matches to biu chun rangers . take the position in 2012  13 record of this row . select the rows whose club record fuzzily matches to citizen . take the position in 2012  13 record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; enrollment } ; 1,285 } = true
Output: the average of the enrollment record of all rows is 1,285 .
eq { count { filter_greater { all_rows ; attendance ; 40000 } } ; 4 } = true
Output: select the rows whose attendance record is greater than 40000 . the number of such rows is 4 .
eq { nth_max { all_rows ; points ; 4 } ; 6 } = true
Output: the 4th maximum points record of all rows is 6 .
and { only { filter_less { all_rows ; share ; 0.2 % } } ; eq { hop { filter_less { all_rows ; share ; 0.2 % } ; party } ; one london } } = true
Output: select the rows whose share record is less than 0.2  . there is only one such row in the table . the party record of this unqiue row is one london .
eq { count { filter_eq { all_rows ; 1992 - 93 ; 37 } } ; 2 } = true
Output: select the rows whose 1992  93 record is equal to 37 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; team ; cd fas } ; goals conceded } ; hop { filter_eq { all_rows ; team ; cd arcense } ; goals conceded } } = true
Output: select the rows whose team record fuzzily matches to cd fas . take the goals conceded record of this row . select the rows whose team record fuzzily matches to cd arcense . take the goals conceded record of this row . the first record is greater than the second record .
and { eq { hop { filter_eq { all_rows ; number & name ; naval store no 161 } ; date } ; hop { filter_eq { all_rows ; number & name ; briggs , dundee no 20 } ; date } } ; and { eq { hop { filter_eq { all_rows ; number & name ; naval store no 161 } ; date } ; 1918 } ; eq { hop { filter_eq { all_rows ; number & name ; briggs , dundee no 20 } ; date } ; 1918 } } } = true
Output: select the rows whose number  name record fuzzily matches to naval store no 161 . take the date record of this row . select the rows whose number  name record fuzzily matches to briggs , dundee no 20 . take the date record of this row . the first record is equal to the second record . the date record of the first row is 1918 . the date record of the second row is 1918 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg } = true
Output: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is mcg .
eq { hop { argmax { all_rows ; result } ; opponent } ; st louis rams } = true
Output: select the row whose result record of all rows is maximum . the opponent record of this row is st louis rams .
eq { hop { argmin { all_rows ; place } ; player } ; gary hallberg } = true
Output: select the row whose place record of all rows is minimum . the player record of this row is gary hallberg .
eq { hop { nth_argmin { all_rows ; opened ( closing date if defunct ) ; 2 } ; track } ; indianapolis speedrome } = true
Output: select the row whose opened  closing date if defunct  record of all rows is 2nd minimum . the track record of this row is indianapolis speedrome .
eq { count { filter_all { all_rows ; no in series } } ; 11 } = true
Output: select the rows whose no in series record is arbitrary . the number of such rows is 11 .
eq { count { filter_eq { all_rows ; results ; re - elected } } ; 10 } = true
Output: select the rows whose results record fuzzily matches to re  elected . the number of such rows is 10 .
greater { hop { filter_eq { all_rows ; call sign ; cimo - fm } ; frequency } ; hop { filter_eq { all_rows ; call sign ; cfak - fm } ; frequency } } = true
Output: select the rows whose call sign record fuzzily matches to cimo  fm . take the frequency record of this row . select the rows whose call sign record fuzzily matches to cfak  fm . take the frequency record of this row . the first record is greater than the second record .
all_eq { all_rows ; field goals ; 0 } = true
Output: for the field goals records of all rows , all of them are equal to 0 .
eq { hop { argmin { all_rows ; date } ; location / attendance } ; conseco fieldhouse 8214 } = true
Output: select the row whose date record of all rows is minimum . the location  attendance record of this row is conseco fieldhouse 8214 .
eq { count { filter_eq { filter_less { all_rows ; home team score ; 14.0 } ; date ; 28 april 1973 } } ; 2 } = true
Output: select the rows whose home team score record is less than 14.0 . among these rows , select the rows whose date record fuzzily matches to 28 april 1973 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; wins } ; player } ; tom kite } = true
Output: select the row whose wins record of all rows is maximum . the player record of this row is tom kite .
and { only { filter_eq { all_rows ; theme ; gold rush era } } ; eq { hop { filter_eq { all_rows ; theme ; gold rush era } ; casino } ; golden nugget } } = true
Output: select the rows whose theme record fuzzily matches to gold rush era . there is only one such row in the table . the casino record of this unqiue row is golden nugget .
round_eq { avg { all_rows ; losses } ; 10.94 } = true
Output: the average of the losses record of all rows is 10.94 .
less { hop { filter_eq { all_rows ; men 's singles ; kasperi salo } ; year } ; hop { filter_eq { all_rows ; men 's singles ; ville lang } ; year } } = true
Output: select the rows whose men s singles record fuzzily matches to kasperi salo . take the year record of this row . select the rows whose men s singles record fuzzily matches to ville lang . take the year record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; downhill ; - } } ; eq { hop { filter_eq { all_rows ; downhill ; - } ; season } ; 1990 } } = true
Output: select the rows whose downhill record is equal to  . there is only one such row in the table . the season record of this unqiue row is 1990 .
round_eq { sum { all_rows ; notes } ; 22:36:29 } = true
Output: the sum of the notes record of all rows is 22:36:29 .
eq { hop { argmax { all_rows ; viewership } ; original title } ; testing times } = true
Output: select the row whose viewership record of all rows is maximum . the original title record of this row is testing times .
eq { count { filter_eq { all_rows ; to par ; -3 } } ; 2 } = true
Output: select the rows whose to par record is equal to 3 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; points } ; performer } ; kiev connolly & the missing passengers } = true
Output: select the row whose points record of all rows is maximum . the performer record of this row is kiev connolly  the missing passengers .
most_greater { all_rows ; height ; 1.9 } = true
Output: for the height records of all rows , most of them are greater than 1.9 .
eq { count { filter_eq { all_rows ; high assists ; earl watson } } ; 13 } = true
Output: select the rows whose high assists record fuzzily matches to earl watson . the number of such rows is 13 .
and { eq { nth_min { all_rows ; date ; 4 } ; 21 july 2013 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; opponent in the final } ; federico delbonis } } = true
Output: the 4th minimum date record of all rows is 21 july 2013 . the opponent in the final record of the row with 4th minimum date record is federico delbonis .
eq { diff { hop { filter_eq { all_rows ; singer ; pedro miguéis } ; points } ; hop { filter_eq { all_rows ; singer ; joão portugal } ; points } } ; 20 } = true
Output: select the rows whose singer record fuzzily matches to pedro miguéis . take the points record of this row . select the rows whose singer record fuzzily matches to joão portugal . take the points record of this row . the first record is 20 larger than the second record .
round_eq { avg { all_rows ; mass ( kg ) } ; 56.32 kg } = true
Output: the average of the mass  kg  record of all rows is 56.32 kg .
most_eq { all_rows ; outcome ; winner } = true
Output: for the outcome records of all rows , most of them fuzzily match to winner .
round_eq { sum { all_rows ; payout ( us ) } ; 16,030,000 } = true
Output: the sum of the payout  us  record of all rows is 16,030,000 .
eq { hop { argmin { all_rows ; launched } ; ship name } ; brp pangasinan } = true
Output: select the row whose launched record of all rows is minimum . the ship name record of this row is brp pangasinan .
most_eq { all_rows ; date ; 30 august 1930 } = true
Output: for the date records of all rows , most of them fuzzily match to 30 august 1930 .
eq { max { filter_eq { all_rows ; gender ; m } ; votes } ; 11542 } = true
Output: select the rows whose gender record fuzzily matches to m . the maximum votes record of these rows is 11542 .
eq { hop { argmax { all_rows ; points } ; artist } ; sasha son } = true
Output: select the row whose points record of all rows is maximum . the artist record of this row is sasha son .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; overton brooks } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is overton brooks .
eq { count { filter_eq { all_rows ; nationality ; jamaica } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to jamaica . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; country ; south africa } } ; 4 } = true
Output: select the rows whose country record fuzzily matches to south africa . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; 5 } = true
Output: select the rows whose platform  s  record fuzzily matches to gamecube . the number of such rows is 5 .
and { only { filter_eq { all_rows ; result ; won } } ; and { eq { hop { filter_eq { all_rows ; result ; won } ; year } ; 1996 } ; eq { hop { filter_eq { all_rows ; result ; won } ; group } ; césar awards } } } = true
Output: select the rows whose result record fuzzily matches to won . there is only one such row in the table . the year record of this unqiue row is 1996 . the group record of this unqiue row is césar awards .
and { only { filter_eq { all_rows ; opponent in the final ; vasilis mazarakis } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; vasilis mazarakis } ; date } ; 20 march 2007 } } = true
Output: select the rows whose opponent in the final record fuzzily matches to vasilis mazarakis . there is only one such row in the table . the date record of this unqiue row is 20 march 2007 .
and { only { filter_eq { all_rows ; chassis ; hwm 53 } } ; eq { hop { filter_eq { all_rows ; chassis ; hwm 53 } ; year } ; 1953 } } = true
Output: 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 .
greater { hop { filter_eq { all_rows ; venue ; lake oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; arden street oval } ; crowd } } = true
Output: select the rows whose venue record fuzzily matches to lake oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to arden street oval . take the crowd record of this row . the first record is greater than the second record .