niryuu/sni-each-converted huggingface dataset:
eq { hop { argmax { all_rows ; population density ( / km 2 ) } ; administrative district } ; jaffna } = true
Output: select the row whose population density   km 2  record of all rows is maximum . the administrative district record of this row is jaffna .
eq { hop { nth_argmax { all_rows ; winners share ; 2 } ; date } ; 12 aug 2007 } = true
Output: select the row whose winners share record of all rows is 2nd maximum . the date record of this row is 12 aug 2007 .
round_eq { avg { all_rows ; total } ; 19.71 } = true
Output: the average of the total record of all rows is 19.71 .
eq { count { filter_eq { all_rows ; date of successors formal installation ; not filled this term } } ; 2 } = true
Output: select the rows whose date of successors formal installation record fuzzily matches to not filled this term . the number of such rows is 2 .
eq { hop { argmax { all_rows ; ties } ; season } ; 2011 } = true
Output: select the row whose ties record of all rows is maximum . the season record of this row is 2011 .
most_eq { all_rows ; club ; rkc waalwijk } = true
Output: for the club records of all rows , most of them fuzzily match to rkc waalwijk .
round_eq { avg { all_rows ; rank } ; 4.89 } = true
Output: the average of the rank record of all rows is 4.89 .
round_eq { avg { all_rows ; score } ; 210.6 } = true
Output: the average of the score record of all rows is 210.6 .
eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; eric leckner } = true
Output: select the row whose years for grizzlies record of all rows is minimum . the player record of this row is eric leckner .
eq { count { filter_eq { all_rows ; license ; closed source } } ; 12 } = true
Output: select the rows whose license record fuzzily matches to closed source . the number of such rows is 12 .
eq { count { filter_eq { all_rows ; commissioned ; december 1945 } } ; 2 } = true
Output: select the rows whose commissioned record fuzzily matches to december 1945 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; player ; nick price } ; wins } ; hop { filter_eq { all_rows ; player ; fred couples } ; wins } } = true
Output: select the rows whose player record fuzzily matches to nick price . take the wins record of this row . select the rows whose player record fuzzily matches to fred couples . take the wins record of this row . the first record is greater than the second record .
round_eq { avg { filter_eq { all_rows ; city ; belgrade } ; average attendance } ; 19,807 } = true
Output: select the rows whose city record fuzzily matches to belgrade . the average of the average attendance record of these rows is 19,807 .
eq { count { filter_greater_eq { all_rows ; height in ft ; 7 - 0 } } ; 2 } = true
Output: select the rows whose height in ft record is greater than or equal to 7  0 . the number of such rows is 2 .
most_less { all_rows ; seating ; 100000 } = true
Output: for the seating records of all rows , most of them are less than 100000 .
eq { count { filter_eq { all_rows ; result ; lost } } ; 3 } = true
Output: select the rows whose result record fuzzily matches to lost . the number of such rows is 3 .
round_eq { avg { all_rows ; erp w } ; 16 } = true
Output: the average of the erp w record of all rows is 16 .
eq { count { filter_eq { filter_eq { all_rows ; date ; august } ; pole position ; petri suvanto } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to august . among these rows , select the rows whose pole position record fuzzily matches to petri suvanto . the number of such rows is 2 .
and { only { filter_eq { all_rows ; author ; barbara clegg and marc platt } } ; eq { hop { filter_eq { all_rows ; author ; barbara clegg and marc platt } ; title } ; point of entry } } = true
Output: select the rows whose author record fuzzily matches to barbara clegg and marc platt . there is only one such row in the table . the title record of this unqiue row is point of entry .
and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; kyoto } } = true
Output: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is kyoto .
eq { hop { argmax { all_rows ; appearances } ; name } ; valdo } = true
Output: select the row whose appearances record of all rows is maximum . the name record of this row is valdo .
and { only { filter_eq { all_rows ; outcome ; runner-up } } ; eq { hop { filter_eq { all_rows ; outcome ; runner-up } ; year } ; 1993 } } = true
Output: select the rows whose outcome record fuzzily matches to runnerup . there is only one such row in the table . the year record of this unqiue row is 1993 .
round_eq { sum { all_rows ; crowd } ; 149299 } = true
Output: the sum of the crowd record of all rows is 149299 .
round_eq { avg { all_rows ; goals } ; 146.8 } = true
Output: the average of the goals record of all rows is 146.8 .
and { only { filter_eq { all_rows ; result ; selected } } ; eq { hop { filter_eq { all_rows ; result ; selected } ; week } ; wild card } } = true
Output: select the rows whose result record fuzzily matches to selected . there is only one such row in the table . the week record of this unqiue row is wild card .
eq { hop { nth_argmax { all_rows ; upper index kcal / nm 3 ; 2 } ; fuel gas } ; iso - butane } = true
Output: select the row whose upper index kcal  nm 3 record of all rows is 2nd maximum . the fuel gas record of this row is iso  butane .
eq { count { filter_less { filter_eq { all_rows ; place ; czechoslovakia } ; date ; 1980 } } ; 3 } = true
Output: select the rows whose place record fuzzily matches to czechoslovakia . among these rows , select the rows whose date record is less than 1980 . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; display ; 6 } ; release date } ; 2012 - 03 } = true
Output: select the row whose display record of all rows is 6th minimum . the release date record of this row is 2012  03 .
greater { hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } } = true
Output: select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; enrollment } ; school } ; new prairie 1 } = true
Output: select the row whose enrollment record of all rows is maximum . the school record of this row is new prairie 1 .
most_eq { all_rows ; rare metals ; no } = true
Output: for the rare metals records of all rows , most of them fuzzily match to no .
eq { hop { argmax { all_rows ; score } ; team 1 } ; ogc nice ( d1 ) } = true
Output: select the row whose score record of all rows is maximum . the team 1 record of this row is ogc nice  d1  .
most_eq { all_rows ; disease area ; cancer } = true
Output: for the disease area records of all rows , most of them fuzzily match to cancer .
eq { count { filter_eq { all_rows ; asts ; 0 } } ; 2 } = true
Output: select the rows whose asts record is equal to 0 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; aspect ; 4:3 } } ; 4 } = true
Output: select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 4 .
most_less { all_rows ; agg ; 4 } = true
Output: for the agg records of all rows , most of them are less than 4 .
and { only { filter_eq { all_rows ; tournaments played ; 19 } } ; eq { hop { filter_eq { all_rows ; tournaments played ; 19 } ; year } ; 2010 } } = true
Output: select the rows whose tournaments played record is equal to 19 . there is only one such row in the table . the year record of this unqiue row is 2010 .
round_eq { sum { all_rows ; score } ; 1419 } = true
Output: the sum of the score record of all rows is 1419 .
eq { hop { nth_argmax { all_rows ; tdp ; 2 } ; brand name ( list ) } ; core i3 - 32xx } = true
Output: select the row whose tdp record of all rows is 2nd maximum . the brand name  list  record of this row is core i3  32xx .
round_eq { avg { all_rows ; spent per voter ( php ) } ; 4.46 } = true
Output: the average of the spent per voter  php  record of all rows is 4.46 .
eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 3 } = true
Output: select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 .
most_greater { all_rows ; gdp ( billion us ) ; 100 } = true
Output: for the gdp  billion us  records of all rows , most of them are greater than 100 .
eq { hop { nth_argmax { all_rows ; sets lost ; 2 } ; team } ; far eastern university } = true
Output: select the row whose sets lost record of all rows is 2nd maximum . the team record of this row is far eastern university .
eq { count { filter_eq { all_rows ; country ; fra } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to fra . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; frequency mhz ; 2 } ; city of license } ; lyons falls , ny } = true
Output: 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 .
less { hop { filter_eq { all_rows ; date ; april 18 } ; score } ; hop { filter_eq { all_rows ; date ; april 21 } ; score } } = true
Output: select the rows whose date record fuzzily matches to april 18 . take the score record of this row . select the rows whose date record fuzzily matches to april 21 . take the score record of this row . the first record is less than the second record .
most_eq { all_rows ; race leader ; luigi marchisio ( ita ) } = true
Output: for the race leader records of all rows , most of them fuzzily match to luigi marchisio  ita  .
eq { hop { nth_argmin { all_rows ; employed ; 2 } ; name } ; zimmerman , roy alfred } = true
Output: select the row whose employed record of all rows is 2nd minimum . the name record of this row is zimmerman , roy alfred .
round_eq { avg { all_rows ; crowd } ; 25000-27000 } = true
Output: the average of the crowd record of all rows is 2500027000 .
eq { count { filter_eq { all_rows ; space agency ; nasa } } ; 3 } = true
Output: select the rows whose space agency record fuzzily matches to nasa . the number of such rows is 3 .
most_eq { all_rows ; party ; democratic party } = true
Output: for the party records of all rows , most of them fuzzily match to democratic party .
and { only { filter_eq { all_rows ; kickoff ; 6:00 pm } } ; eq { hop { filter_eq { all_rows ; kickoff ; 6:00 pm } ; date } ; sunday , april 28 } } = true
Output: select the rows whose kickoff record fuzzily matches to 6:00 pm . there is only one such row in the table . the date record of this unqiue row is sunday , april 28 .
eq { hop { argmax { all_rows ; total assets ( score ) } ; bank } ; bank of industry and mine } = true
Output: select the row whose total assets  score  record of all rows is maximum . the bank record of this row is bank of industry and mine .
greater { hop { filter_eq { all_rows ; nbr class ; 251 } ; total } ; hop { filter_eq { all_rows ; nbr class ; 229 } ; total } } = true
Output: select the rows whose nbr class record fuzzily matches to 251 . take the total record of this row . select the rows whose nbr class record fuzzily matches to 229 . take the total record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; built year ; 2 } ; warship } ; covadonga } = true
Output: select the row whose built year record of all rows is 2nd minimum . the warship record of this row is covadonga .
eq { hop { nth_argmax { filter_greater_eq { all_rows ; won ( pg ) ; 5 } ; goals scored ( gf ) ; 2 } ; played ( pj ) } ; 13 } = true
Output: select the rows whose won  pg  record is greater than or equal to 5 . select the row whose goals scored  gf  record of these rows is 2nd maximum . the played  pj  record of this row is 13 .
eq { count { filter_eq { all_rows ; categoría ; rock group } } ; 2 } = true
Output: select the rows whose categoría record fuzzily matches to rock group . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; founding university ; city college of new york } } ; 2 } = true
Output: select the rows whose founding university record fuzzily matches to city college of new york . the number of such rows is 2 .
and { only { filter_eq { filter_eq { all_rows ; date ; nov } ; result ; loss } } ; eq { hop { filter_eq { filter_eq { all_rows ; date ; nov } ; result ; loss } ; opponent } ; 1 tennessee } } = true
Output: select the rows whose date record fuzzily matches to nov . among these rows , select the rows whose result record fuzzily matches to loss . there is only one such row in the table . the opponent record of this unqiue row is 1 tennessee .
most_greater { all_rows ; date ; 2000 } = true
Output: for the date records of all rows , most of them are greater than 2000 .
most_eq { all_rows ; type ; sovereign } = true
Output: for the type records of all rows , most of them fuzzily match to sovereign .
round_eq { sum { all_rows ; loans received , 3q } ; 8550000 } = true
Output: the sum of the loans received , 3q record of all rows is 8550000 .
all_eq { all_rows ; signed ; no } = true
Output: for the signed records of all rows , all of them fuzzily match to no .
and { only { filter_eq { all_rows ; venue ; toowoomba } } ; eq { hop { filter_eq { all_rows ; venue ; toowoomba } ; date } ; 25 may 1988 } } = true
Output: select the rows whose venue record fuzzily matches to toowoomba . there is only one such row in the table . the date record of this unqiue row is 25 may 1988 .
most_less { all_rows ; population ; 1000 } = true
Output: for the population records of all rows , most of them are less than 1000 .
and { only { filter_eq { all_rows ; country ; finland } } ; eq { hop { filter_eq { all_rows ; country ; finland } ; company } ; stonesoft } } = true
Output: select the rows whose country record fuzzily matches to finland . there is only one such row in the table . the company record of this unqiue row is stonesoft .
and { eq { max { all_rows ; frequency mhz } ; 107.3 } ; eq { hop { argmax { all_rows ; frequency mhz } ; city of license } ; hill city , kansas } } = true
Output: the maximum frequency mhz record of all rows is 107.3 . the city of license record of the row with superlative frequency mhz record is hill city , kansas .
eq { count { filter_greater { all_rows ; year ; 2000 } } ; 3 } = true
Output: select the rows whose year record is greater than 2000 . the number of such rows is 3 .
most_eq { all_rows ; discipline ; stock car } = true
Output: for the discipline records of all rows , most of them fuzzily match to stock car .
most_less { all_rows ; 3:26.00 ; 3:30 } = true
Output: for the 3:26.00 records of all rows , most of them are less than 3:30 .
eq { hop { argmax { all_rows ; points } ; artist } ; jean - claude pascal } = true
Output: select the row whose points record of all rows is maximum . the artist record of this row is jean  claude pascal .
and { only { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } } ; eq { hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect } ; john cantine } } = true
Output: select the rows whose reason for non  seating record fuzzily matches to elected , but declined to take office . there is only one such row in the table . the member  elect record of this unqiue row is john cantine .
and { only { filter_greater { all_rows ; padilla municipality ; 10000 } } ; eq { hop { filter_greater { all_rows ; padilla municipality ; 10000 } ; language } ; spanish } } = true
Output: select the rows whose padilla municipality record is greater than 10000 . there is only one such row in the table . the language record of this unqiue row is spanish .
eq { count { filter_eq { filter_eq { all_rows ; surface ; clay } ; week ; may } } ; 2 } = true
Output: select the rows whose surface record fuzzily matches to clay . among these rows , select the rows whose week record fuzzily matches to may . the number of such rows is 2 .
eq { hop { argmax { all_rows ; total } ; gold } ; 3 } = true
Output: select the row whose total record of all rows is maximum . the gold record of this row is 3 .
eq { count { filter_eq { all_rows ; game site ; the meadowlands } } ; 8 } = true
Output: select the rows whose game site record fuzzily matches to the meadowlands . the number of such rows is 8 .
and { only { filter_eq { all_rows ; succeeded by ; no successor } } ; eq { hop { filter_eq { all_rows ; succeeded by ; no successor } ; headphone model } ; hp1000 } } = true
Output: select the rows whose succeeded by record fuzzily matches to no successor . there is only one such row in the table . the headphone model record of this unqiue row is hp1000 .
round_eq { avg { all_rows ; assists } ; 64.2 } = true
Output: the average of the assists record of all rows is 64.2 .
greater { hop { filter_eq { all_rows ; year ; 1931 } ; laps } ; hop { filter_eq { all_rows ; year ; 1930 } ; laps } } = true
Output: select the rows whose year record fuzzily matches to 1931 . take the laps record of this row . select the rows whose year record fuzzily matches to 1930 . take the laps record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; points } ; year } ; 1993 } = true
Output: select the row whose points record of all rows is maximum . the year record of this row is 1993 .
and { only { filter_eq { all_rows ; head coach ; neven spahija } } ; eq { hop { filter_eq { all_rows ; head coach ; neven spahija } ; season } ; 2005 - 06 } } = true
Output: select the rows whose head coach record fuzzily matches to neven spahija . there is only one such row in the table . the season record of this unqiue row is 2005  06 .
and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo } } = true
Output: select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is são paulo .
most_eq { filter_eq { all_rows ; location ; philips arena } ; decision ; o pavelec } = true
Output: select the rows whose location record fuzzily matches to philips arena . for the decision records of these rows , most of them fuzzily match to o pavelec .
eq { count { filter_eq { all_rows ; frequency ; fm } } ; 9 } = true
Output: select the rows whose frequency record fuzzily matches to fm . the number of such rows is 9 .
round_eq { sum { all_rows ; crowd } ; 91651 } = true
Output: the sum of the crowd record of all rows is 91651 .
eq { hop { argmax { all_rows ; total } ; name } ; ivo lakučs ( lat ) } = true
Output: select the row whose total record of all rows is maximum . the name record of this row is ivo lakučs  lat  .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; host } ; marty reid } = true
Output: select the row whose year record of all rows is 2nd maximum . the host record of this row is marty reid .
round_eq { avg { all_rows ; age } ; 23.7 } = true
Output: the average of the age record of all rows is 23.7 .
most_eq { filter_eq { all_rows ; date ; october } ; result ; l } = true
Output: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to l .
eq { hop { argmax { all_rows ; duration } ; start date / time } ; 13 may 21:17 } = true
Output: select the row whose duration record of all rows is maximum . the start date  time record of this row is 13 may 21:17 .
round_eq { avg { all_rows ; horsepowers } ; 7031 } = true
Output: the average of the horsepowers record of all rows is 7031 .
eq { hop { argmax { all_rows ; attendance } ; date } ; november 14 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is november 14 .
most_eq { all_rows ; label ; rough trade records } = true
Output: for the label records of all rows , most of them fuzzily match to rough trade records .
eq { count { filter_eq { all_rows ; location ; nevada , united states } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to nevada , united states . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; name ; chopra } ; started } ; hop { filter_eq { all_rows ; name ; routledge } ; started } } = true
Output: select the rows whose name record fuzzily matches to chopra . take the started record of this row . select the rows whose name record fuzzily matches to routledge . take the started record of this row . the first record is less than the second record .
eq { count { filter_eq { filter_eq { all_rows ; directed by ; stephen kay } ; written by ; norman morrill } } ; 2 } = true
Output: select the rows whose directed by record fuzzily matches to stephen kay . among these rows , select the rows whose written by record fuzzily matches to norman morrill . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; earnings ; 3 } ; player } ; arnold palmer } = true
Output: select the row whose earnings record of all rows is 3rd maximum . the player record of this row is arnold palmer .
eq { count { filter_greater { all_rows ; crowd ; 10000 } } ; 2 } = true
Output: select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .
most_eq { all_rows ; event ; freestyle } = true
Output: for the event records of all rows , most of them fuzzily match to freestyle .
eq { count { filter_eq { all_rows ; livery ; fragonset black } } ; 3 } = true
Output: select the rows whose livery record fuzzily matches to fragonset black . the number of such rows is 3 .
and { only { filter_less { all_rows ; laps ; 17 } } ; eq { hop { filter_less { all_rows ; laps ; 17 } ; year } ; 1966 } } = true
Output: select the rows whose laps record is less than 17 . there is only one such row in the table . the year record of this unqiue row is 1966 .
and { only { filter_eq { all_rows ; goals ; 23 } } ; eq { hop { filter_eq { all_rows ; goals ; 23 } ; player } ; mehmet yılmaz } } = true
Output: 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 .
all_eq { all_rows ; date ; 28 august 1954 } = true
Output: for the date records of all rows , all of them fuzzily match to 28 august 1954 .
and { only { filter_not_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_not_eq { all_rows ; wins ; 0 } ; tournament } ; pga championship } } = true
Output: select the rows whose wins record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .
eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; jason clemett } = true
Output: select the row whose pick record of all rows is 2nd maximum . the player record of this row is jason clemett .
most_less { all_rows ; overall ; 10 } = true
Output: for the overall records of all rows , most of them are less than 10 .
most_greater { filter_greater { all_rows ; points - ; 1200 } ; points + ; 1000 } = true
Output: select the rows whose points  record is greater than 1200 . for the points + records of these rows , most of them are greater than 1000 .
and { only { filter_eq { all_rows ; earpads ; circumaural bowl } } ; eq { hop { filter_eq { all_rows ; earpads ; circumaural bowl } ; headphone model } ; gs1000i } } = true
Output: select the rows whose earpads record fuzzily matches to circumaural bowl . there is only one such row in the table . the headphone model record of this unqiue row is gs1000i .
eq { hop { nth_argmax { all_rows ; runs ( balls ) ; 3 } ; partnerships } ; herschelle gibbs / justin kemp } = true
Output: select the row whose runs  balls  record of all rows is 3rd maximum . the partnerships record of this row is herschelle gibbs  justin kemp .
greater { hop { filter_eq { all_rows ; artist ; andreas lundstedt } ; points } ; hop { filter_eq { all_rows ; artist ; garmarna } ; points } } = true
Output: select the rows whose artist record fuzzily matches to andreas lundstedt . take the points record of this row . select the rows whose artist record fuzzily matches to garmarna . take the points record of this row . the first record is greater than the second record .
all_eq { all_rows ; date ; december } = true
Output: for the date records of all rows , all of them fuzzily match to december .
and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; date } ; 19 june 2005 } } = true
Output: select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the date record of this unqiue row is 19 june 2005 .
round_eq { sum { all_rows ; crowd } ; 78,000 } = true
Output: the sum of the crowd record of all rows is 78,000 .
eq { hop { nth_argmin { filter_eq { all_rows ; college / junior / club team ( league ) ; university of notre dame ( ccha ) } ; round ; 2 } ; player } ; ben ryan } = true
Output: select the rows whose college  junior  club team  league  record fuzzily matches to university of notre dame  ccha  . select the row whose round record of these rows is 2nd minimum . the player record of this row is ben ryan .
eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; rebel without a pause } = true
Output: select the row whose us viewers  in millions  record of all rows is maximum . the title record of this row is rebel without a pause .
most_greater { all_rows ; crowd ; 16000 } = true
Output: for the crowd records of all rows , most of them are greater than 16000 .
most_less { all_rows ; preliminary average ; 9.0 } = true
Output: for the preliminary average records of all rows , most of them are less than 9.0 .
eq { hop { nth_argmax { all_rows ; total passengers ; 3 } ; rank } ; 3 } = true
Output: select the row whose total passengers record of all rows is 3rd maximum . the rank record of this row is 3 .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; visitor } ; anaheim } = true
Output: select the row whose attendance record of all rows is 1st maximum . the visitor record of this row is anaheim .
and { only { filter_eq { all_rows ; category ; newcomer } } ; and { eq { hop { filter_eq { all_rows ; category ; newcomer } ; year } ; 2008 } ; eq { hop { filter_eq { all_rows ; category ; newcomer } ; result } ; won } } } = true
Output: select the rows whose category record fuzzily matches to newcomer . there is only one such row in the table . the year record of this unqiue row is 2008 . the result record of this unqiue row is won .
eq { hop { nth_argmax { filter_eq { all_rows ; gold ; 0 } ; total ; 2 } ; nation } ; slovakia } = true
Output: select the rows whose gold record is equal to 0 . select the row whose total record of these rows is 2nd maximum . the nation record of this row is slovakia .
most_greater { all_rows ; attendance ; 20000 } = true
Output: for the attendance records of all rows , most of them are greater than 20000 .
round_eq { sum { all_rows ; quantity } ; 427 } = true
Output: the sum of the quantity record of all rows is 427 .
and { only { filter_eq { all_rows ; result ; 28 - 17 } } ; eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 } } = true
Output: select the rows whose result record fuzzily matches to 28  17 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1962 .
and { only { filter_eq { all_rows ; points ; 16 } } ; eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano } } = true
Output: select the rows whose points record is equal to 16 . there is only one such row in the table . the team record of this unqiue row is ec são caetano .
less { hop { filter_eq { all_rows ; year ; 2004 } ; rank } ; hop { filter_eq { all_rows ; year ; 2006 } ; rank } } = true
Output: select the rows whose year record fuzzily matches to 2004 . take the rank record of this row . select the rows whose year record fuzzily matches to 2006 . take the rank record of this row . the first record is less than the second record .
most_eq { all_rows ; extra ; team competition } = true
Output: for the extra records of all rows , most of them fuzzily match to team competition .
eq { count { filter_greater { filter_eq { all_rows ; year ; 2010 } ; races ; 10 } } ; 2 } = true
Output: select the rows whose year record is equal to 2010 . among these rows , select the rows whose races record is greater than 10 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; blocks per game } ; month } ; december 2007 } = true
Output: select the row whose blocks per game record of all rows is maximum . the month record of this row is december 2007 .
all_eq { all_rows ; national team ; brazil } = true
Output: for the national team records of all rows , all of them fuzzily match to brazil .
round_eq { avg { all_rows ; attendance } ; 17392 } = true
Output: the average of the attendance record of all rows is 17392 .
eq { count { filter_eq { all_rows ; partner ; philipp oswald } } ; 2 } = true
Output: select the rows whose partner record fuzzily matches to philipp oswald . the number of such rows is 2 .
round_eq { sum { filter_less_eq { all_rows ; date ; february 5 } ; attendance } ; 37097 } = true
Output: select the rows whose date record is less than or equal to february 5 . the sum of the attendance record of these rows is 37097 .
less { hop { filter_eq { all_rows ; team ; salisbury united } ; founded } ; hop { filter_eq { all_rows ; team ; south adelaide } ; founded } } = true
Output: select the rows whose team record fuzzily matches to salisbury united . take the founded record of this row . select the rows whose team record fuzzily matches to south adelaide . take the founded record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; score } ; 4.21 } = true
Output: the average of the score record of all rows is 4.21 .
eq { count { filter_eq { all_rows ; opponent ; new york } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to new york . the number of such rows is 2 .
round_eq { sum { filter_less { all_rows ; rank ; 6 } ; gold } ; 42 } = true
Output: select the rows whose rank record is less than 6 . the sum of the gold record of these rows is 42 .
eq { hop { argmax { all_rows ; sunlight hours } ; city / town } ; pontevedra } = true
Output: select the row whose sunlight hours record of all rows is maximum . the city  town record of this row is pontevedra .
eq { hop { argmax { all_rows ; goals against } ; team } ; montreal crystals } = true
Output: select the row whose goals against record of all rows is maximum . the team record of this row is montreal crystals .
and { only { filter_greater { all_rows ; score ; 400 } } ; eq { hop { filter_greater { all_rows ; score ; 400 } ; batsmen } ; adrian dale and viv richards } } = true
Output: select the rows whose score record is greater than 400 . there is only one such row in the table . the batsmen record of this unqiue row is adrian dale and viv richards .
most_eq { all_rows ; round ; 1 } = true
Output: for the round records of all rows , most of them are equal to 1 .
most_greater { all_rows ; score ; 10 } = true
Output: for the score records of all rows , most of them are greater than 10 .
and { only { filter_eq { filter_eq { all_rows ; apps ; 38 } ; points ; 91 } } ; eq { hop { filter_eq { filter_eq { all_rows ; apps ; 38 } ; points ; 91 } ; club } ; barcelona } } = true
Output: select the rows whose apps record is equal to 38 . among these rows , select the rows whose points record is equal to 91 . there is only one such row in the table . the club record of this unqiue row is barcelona .
and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } } } = true
Output: select the rows whose score record fuzzily matches to 3  0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3  2 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
less { hop { filter_eq { all_rows ; visitor ; cavaliers } ; date } ; hop { filter_eq { all_rows ; visitor ; hornets } ; date } } = true
Output: select the rows whose visitor record fuzzily matches to cavaliers . take the date record of this row . select the rows whose visitor record fuzzily matches to hornets . take the date record of this row . the first record is less than the second record .
most_greater { all_rows ; goals for ; 50 } = true
Output: for the goals for records of all rows , most of them are greater than 50 .
eq { count { filter_eq { all_rows ; location ; london , uk } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to london , uk . the number of such rows is 3 .
and { only { filter_less { all_rows ; winning score ; 180 } } ; eq { hop { filter_less { all_rows ; winning score ; 180 } ; tournament } ; charles schwab cup championship } } = true
Output: select the rows whose winning score record is less than 180 . there is only one such row in the table . the tournament record of this unqiue row is charles schwab cup championship .
most_eq { all_rows ; results ; re - elected } = true
Output: for the results records of all rows , most of them fuzzily match to re  elected .
eq { hop { argmax { all_rows ; viewers ( millions ) } ; title } ; about face } = true
Output: select the row whose viewers  millions  record of all rows is maximum . the title record of this row is about face .
greater { hop { filter_eq { all_rows ; cartridge ; .357 sig } ; max pressure } ; hop { filter_eq { all_rows ; cartridge ; .380 acp } ; max pressure } } = true
Output: select the rows whose cartridge record fuzzily matches to .357 sig . take the max pressure record of this row . select the rows whose cartridge record fuzzily matches to .380 acp . take the max pressure record of this row . the first record is greater than the second record .
most_eq { all_rows ; leading scorer ; tim duncan } = true
Output: for the leading scorer records of all rows , most of them fuzzily match to tim duncan .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; october 7 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is october 7 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; vfl park } = true
Output: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is vfl park .
eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 2 } ; film title used in nomination } ; lore } = true
Output: select the row whose year  ceremony  record of all rows is 2nd maximum . the film title used in nomination record of this row is lore .
eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 4 } = true
Output: select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 4 .
and { only { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } } ; eq { hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date } ; 28 july 2011 } } = true
Output: select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification . there is only one such row in the table . the date record of this unqiue row is 28 july 2011 .
eq { hop { nth_argmax { filter_eq { all_rows ; bronze ; 2 } ; gold ; 1 } ; rank } ; 4 } = true
Output: select the rows whose bronze record is equal to 2 . select the row whose gold record of these rows is 1st maximum . the rank record of this row is 4 .
eq { count { filter_eq { all_rows ; release ; classic electro } } ; 4 } = true
Output: select the rows whose release record fuzzily matches to classic electro . the number of such rows is 4 .
greater { hop { filter_eq { all_rows ; nation ; australia ( aus ) } ; silver } ; hop { filter_eq { all_rows ; nation ; japan ( jpn ) } ; silver } } = true
Output: select the rows whose nation record fuzzily matches to australia  aus  . take the silver record of this row . select the rows whose nation record fuzzily matches to japan  jpn  . take the silver record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; worldwide gross ; 3 } ; title } ; mission : impossible } = true
Output: select the row whose worldwide gross record of all rows is 3rd maximum . the title record of this row is mission : impossible .
eq { hop { nth_argmax { all_rows ; date won ; 4 } ; champion ( s ) } ; atomo & sonico } = true
Output: select the row whose date won record of all rows is 4th maximum . the champion  s  record of this row is atomo  sonico .
and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; artist } ; photogenique } } = true
Output: 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 .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; july 21 , 2007 } = true
Output: select the row whose attendance record of all rows is 1st maximum . the date record of this row is july 21 , 2007 .
round_eq { avg { all_rows ; viewing figure } ; 6.40 million } = true
Output: the average of the viewing figure record of all rows is 6.40 million .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; jamie l whitten } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is jamie l whitten .
most_eq { all_rows ; city ; los angeles , california } = true
Output: for the city records of all rows , most of them fuzzily match to los angeles , california .
eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; united states } = true
Output: select the row whose time record of all rows is 3rd minimum . the country record of this row is united states .
and { only { filter_greater { all_rows ; lead ; 25 % } } ; eq { hop { filter_greater { all_rows ; lead ; 25 % } ; date released } ; july 19 , 1987 } } = true
Output: select the rows whose lead record is greater than 25  . there is only one such row in the table . the date released record of this unqiue row is july 19 , 1987 .
eq { hop { argmax { all_rows ; top - 25 } ; tournament } ; the open championship } = true
Output: select the row whose top  25 record of all rows is maximum . the tournament record of this row is the open championship .
eq { hop { nth_argmax { all_rows ; episode ; 2 } ; segment b } ; compact track loaders } = true
Output: select the row whose episode record of all rows is 2nd maximum . the segment b record of this row is compact track loaders .
and { only { filter_eq { all_rows ; college ; arkansas state } } ; eq { hop { filter_eq { all_rows ; college ; arkansas state } ; name } ; robert speer } } = true
Output: select the rows whose college record fuzzily matches to arkansas state . there is only one such row in the table . the name record of this unqiue row is robert speer .
eq { count { filter_eq { all_rows ; partner ; helena suková } } ; 3 } = true
Output: select the rows whose partner record fuzzily matches to helena suková . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; date } ; 12 august 2007 } = true
Output: select the row whose crowd record of all rows is 1st maximum . the date record of this row is 12 august 2007 .
eq { hop { nth_argmax { all_rows ; population ( 2011 ) ; 2 } ; urban settlement } ; ruma } = true
Output: select the row whose population  2011  record of all rows is 2nd maximum . the urban settlement record of this row is ruma .
greater { hop { filter_eq { all_rows ; tournament ; jordan fa shield } ; draws } ; hop { filter_eq { all_rows ; tournament ; jordan super cup } ; draws } } = true
Output: select the rows whose tournament record fuzzily matches to jordan fa shield . take the draws record of this row . select the rows whose tournament record fuzzily matches to jordan super cup . take the draws record of this row . the first record is greater than the second record .
only { filter_eq { all_rows ; surface ; hard ( i ) } } = true
Output: select the rows whose surface record fuzzily matches to hard  i  . there is only one such row in the table .
eq { count { filter_all { all_rows ; country } } ; 5 } = true
Output: select the rows whose country record is arbitrary . the number of such rows is 5 .
and { only { filter_greater { all_rows ; capacity ; 16000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 16000 } ; stadium } ; ratina stadion } } = true
Output: select the rows whose capacity record is greater than 16000 . there is only one such row in the table . the stadium record of this unqiue row is ratina stadion .
round_eq { avg { all_rows ; weight ( kg ) } ; 54.29 } = true
Output: the average of the weight  kg  record of all rows is 54.29 .
most_eq { all_rows ; winning car ; lotus } = true
Output: for the winning car records of all rows , most of them fuzzily match to lotus .
and { only { filter_eq { all_rows ; competition ; euro } } ; eq { hop { filter_eq { all_rows ; competition ; euro } ; date } ; 29 october 1975 } } = true
Output: select the rows whose competition record fuzzily matches to euro . there is only one such row in the table . the date record of this unqiue row is 29 october 1975 .
most_eq { all_rows ; original channel ; itv } = true
Output: for the original channel records of all rows , most of them fuzzily match to itv .
greater { hop { filter_eq { all_rows ; english title ; the saviour of the soul } ; hk viewers } ; hop { filter_eq { all_rows ; english title ; men in pain } ; hk viewers } } = true
Output: select the rows whose english title record fuzzily matches to the saviour of the soul . take the hk viewers record of this row . select the rows whose english title record fuzzily matches to men in pain . take the hk viewers record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; res ; win } } ; 4 } = true
Output: select the rows whose res record fuzzily matches to win . the number of such rows is 4 .
greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } } = true
Output: select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; location attendance ; chicago stadium } } ; eq { hop { filter_eq { all_rows ; location attendance ; chicago stadium } ; date } ; january 11 } } = true
Output: select the rows whose location attendance record fuzzily matches to chicago stadium . there is only one such row in the table . the date record of this unqiue row is january 11 .
and { eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; home team score } ; 13.7 ( 85 ) } ; eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; away team } ; st kilda } } = true
Output: select the row whose crowd record of all rows is 3rd maximum . the home team score record of this row is 13.7  85  . the away team record of this row is st kilda .
and { only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg } } = true
Output: select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table . the venue record of this unqiue row is mcg .
and { only { filter_eq { all_rows ; average ratings ; 14.2 % } } ; eq { hop { filter_eq { all_rows ; average ratings ; 14.2 % } ; romaji title } ; sapuri } } = true
Output: select the rows whose average ratings record fuzzily matches to 14.2  . there is only one such row in the table . the romaji title record of this unqiue row is sapuri .
eq { diff { hop { filter_eq { all_rows ; incumbent ; james o'connor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; henry garland dupré } ; first elected } } ; 10 } = true
Output: select the rows whose incumbent record fuzzily matches to james oconnor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to henry garland dupré . take the first elected record of this row . the first record is 10 larger than the second record .
all_eq { filter_greater { all_rows ; races ; 20 } ; wins ; 0 } = true
Output: select the rows whose races record is greater than 20 . for the wins records of these rows , all of them are equal to 0 .
and { eq { nth_max { all_rows ; crowd ; 3 } ; 20974 } ; eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; date } ; 7 june 1926 } } = true
Output: the 3rd maximum crowd record of all rows is 20974 . the date record of the row with 3rd maximum crowd record is 7 june 1926 .
most_eq { filter_eq { all_rows ; gender ; male } ; status ; dead } = true
Output: select the rows whose gender record fuzzily matches to male . for the status records of these rows , most of them fuzzily match to dead .
eq { count { filter_eq { all_rows ; format ; vinyl record } } ; 2 } = true
Output: select the rows whose format record fuzzily matches to vinyl record . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; first broadcast ; september 2013 } } ; 4 } = true
Output: select the rows whose first broadcast record fuzzily matches to september 2013 . the number of such rows is 4 .
most_greater { all_rows ; points ; 50 } = true
Output: for the points records of all rows , most of them are greater than 50 .
eq { count { filter_eq { all_rows ; result ; l } } ; 8 } = true
Output: select the rows whose result record fuzzily matches to l . the number of such rows is 8 .
round_eq { sum { filter_greater_eq { all_rows ; points ; 10 } ; against } ; 30 } = true
Output: select the rows whose points record is greater than or equal to 10 . the sum of the against record of these rows is 30 .
eq { count { filter_eq { all_rows ; event ; beijing } } ; 6 } = true
Output: select the rows whose event record fuzzily matches to beijing . the number of such rows is 6 .
eq { hop { nth_argmax { all_rows ; weeks ; 2 } ; title } ; evolution } = true
Output: select the row whose weeks record of all rows is 2nd maximum . the title record of this row is evolution .
round_eq { avg { all_rows ; crowd } ; 22250 } = true
Output: the average of the crowd record of all rows is 22250 .
and { only { filter_eq { all_rows ; chassis ; ferrari 156 aero } } ; eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 } } = true
Output: select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table . the year record of this unqiue row is 1964 .
eq { nth_min { all_rows ; date ; 1 } ; july 23 , 2002 } = true
Output: the 1st minimum date record of all rows is july 23 , 2002 .
round_eq { sum { all_rows ; 1st prize } ; 166,000 } = true
Output: the sum of the 1st prize record of all rows is 166,000 .
and { only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } } ; eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 } } = true
Output: select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table . the stage record of this unqiue row is 5 .
most_eq { all_rows ; team ; team impul } = true
Output: for the team records of all rows , most of them fuzzily match to team impul .