niryuu/sni-each-converted huggingface dataset:
select the row whose total passengers record of all rows is 3rd maximum . the rank record of this row is 3 .
Output: eq { hop { nth_argmax { all_rows ; total passengers ; 3 } ; rank } ; 3 }
select the rows whose manufacturer record fuzzily matches to beyer , peacock & co . among these rows , select the rows whose manufacturer record fuzzily matches to beyer , peacock & co . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; manufacturer ; beyer , peacock & co } ; manufacturer ; beyer , peacock & co } } ; 3 }
the maximum points record of all rows is 11 . the entrant record of the row with superlative points record is team lotus .
Output: and { eq { max { all_rows ; points } ; 11 } ; eq { hop { argmax { all_rows ; points } ; entrant } ; team lotus } }
select the rows whose event record fuzzily matches to ifc wc 13 - warriors challenge . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; event ; ifc wc 13 - warriors challenge } } ; 2 }
select the rows whose runners up record fuzzily matches to canterbury - bankstown bulldogs . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; runners up ; canterbury - bankstown bulldogs } } ; 2 }
for the high assists records of all rows , most of them fuzzily match to caron butler .
Output: most_eq { all_rows ; high assists ; caron butler }
select the rows whose written by record fuzzily matches to dan schneider . for the us viewers ( millions ) records of these rows , most of them are less than 5 million .
Output: most_less { filter_eq { all_rows ; written by ; dan schneider } ; us viewers ( millions ) ; 5 million }
select the rows whose location record fuzzily matches to china . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; location ; china } } ; 6 }
select the rows whose result record fuzzily matches to lost renomination democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is clyde l garrett .
Output: and { only { filter_eq { all_rows ; result ; lost renomination democratic hold } } ; eq { hop { filter_eq { all_rows ; result ; lost renomination democratic hold } ; incumbent } ; clyde l garrett } }
select the rows whose capital record fuzzily matches to warsaw . select the row whose area in 1000 km square ( 1930 ) record of these rows is 1st maximum . the voivodeship separate city record of this row is warszawskie .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; capital ; warsaw } ; area in 1000 km square ( 1930 ) ; 1 } ; voivodeship separate city } ; warszawskie }
select the row whose silver record of all rows is 2nd maximum . the nation record of this row is canada .
Output: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; canada }
select the rows whose country record fuzzily matches to brazil . take the change ( 2011 to 2012 ) record of this row . select the rows whose country record fuzzily matches to canada . take the change ( 2011 to 2012 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; country ; brazil } ; change ( 2011 to 2012 ) } ; hop { filter_eq { all_rows ; country ; canada } ; change ( 2011 to 2012 ) } }
select the rows whose city / town record fuzzily matches to wabash . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; city / town ; wabash } } ; 4 }
select the rows whose country record fuzzily matches to iran . take the 2011 gdp ( ppp ) billions of usd record of this row . select the rows whose country record fuzzily matches to lebanon . take the 2011 gdp ( ppp ) billions of usd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; country ; iran } ; 2011 gdp ( ppp ) billions of usd } ; hop { filter_eq { all_rows ; country ; lebanon } ; 2011 gdp ( ppp ) billions of usd } }
select the rows whose country record fuzzily matches to united states . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 2 }
select the rows whose currency record fuzzily matches to latvian lats . take the official target date record of this row . select the rows whose currency record fuzzily matches to lithuanian litas . take the official target date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; currency ; latvian lats } ; official target date } ; hop { filter_eq { all_rows ; currency ; lithuanian litas } ; official target date } }
select the rows whose owner record fuzzily matches to royal thai army . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; owner ; royal thai army } } ; 2 }
select the rows whose international tourist arrivals ( 2012 ) record is greater than 40 million . select the row whose change ( 2011 to 2012 ) record of these rows is 1st maximum . the country record of this row is spain .
Output: eq { hop { nth_argmax { filter_greater { all_rows ; international tourist arrivals ( 2012 ) ; 40 million } ; change ( 2011 to 2012 ) ; 1 } ; country } ; spain }
select the row whose date record of all rows is 3rd minimum . the race record of this row is san marino grand prix .
Output: eq { hop { nth_argmin { all_rows ; date ; 3 } ; race } ; san marino grand prix }
select the rows whose date successor seated record fuzzily matches to november . among these rows , select the rows whose successor record fuzzily matches to ( r ) . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date successor seated ; november } ; successor ; ( r ) } } ; 2 }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is wright patman .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; wright patman }
select the rows whose nation record fuzzily matches to guatemala . take the silver record of this row . select the rows whose nation record fuzzily matches to el salvador . take the silver record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; nation ; guatemala } ; silver } ; hop { filter_eq { all_rows ; nation ; el salvador } ; silver } } ; 1 }
select the rows whose gold record fuzzily matches to wu yanan . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; gold ; wu yanan } } ; 2 }
select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is rhein fire .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; rhein fire }
for the capacity records of all rows , most of them are greater than 40000 .
Output: most_greater { all_rows ; capacity ; 40000 }
select the rows whose platform ( s ) record fuzzily matches to wii . there is only one such row in the table . the game record of this unqiue row is super mario galaxy 2 .
Output: and { only { filter_eq { all_rows ; platform ( s ) ; wii } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 } }
select the rows whose date record fuzzily matches to april . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; date ; april } } ; 4 }
for the new entries this round records of all rows , most of them fuzzily match to none .
Output: most_eq { all_rows ; new entries this round ; none }
select the row whose score record of all rows is maximum . the date record of this row is 9 october 2012 .
Output: eq { hop { argmax { all_rows ; score } ; date } ; 9 october 2012 }
select the rows whose result record fuzzily matches to bottom . there is only one such row in the table . the theme record of this unqiue row is country pop .
Output: and { only { filter_eq { all_rows ; result ; bottom } } ; eq { hop { filter_eq { all_rows ; result ; bottom } ; theme } ; country pop } }
for the top division titles records of all rows , most of them are less than 10 .
Output: most_less { all_rows ; top division titles ; 10 }
select the row whose regular season 1 record of all rows is minimum . the season record of this row is 2004 - 05 .
Output: eq { hop { argmin { all_rows ; regular season 1 } ; season } ; 2004 - 05 }
for the completion records of all rows , most of them are greater than or equal to 2000 .
Output: most_greater_eq { all_rows ; completion ; 2000 }
select the rows whose elimination move record fuzzily matches to pinfall after a diving headbutt . there is only one such row in the table . the wrestler record of this unqiue row is zach gowen .
Output: and { only { filter_eq { all_rows ; elimination move ; pinfall after a diving headbutt } } ; eq { hop { filter_eq { all_rows ; elimination move ; pinfall after a diving headbutt } ; wrestler } ; zach gowen } }
select the rows whose broadcast record fuzzily matches to espn . for the attendance records of these rows , most of them are less than 100000 .
Output: most_less { filter_eq { all_rows ; broadcast ; espn } ; attendance ; 100000 }
the average of the quantity record of all rows is 25.14 .
Output: round_eq { avg { all_rows ; quantity } ; 25.14 }
select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is mark hensby .
Output: and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; mark hensby } }
select the rows whose competition record fuzzily matches to 2014 world cup qualifier . take the goal record of this row . select the rows whose competition record fuzzily matches to 2011 copa américa . take the goal record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; competition ; 2014 world cup qualifier } ; goal } ; hop { filter_eq { all_rows ; competition ; 2011 copa américa } ; goal } }
select the row whose date record of all rows is 1st minimum . the race name record of this row is datsun twin 200 .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; race name } ; datsun twin 200 }
select the row whose winnings record of all rows is maximum . the year record of this row is 1994 .
Output: eq { hop { argmax { all_rows ; winnings } ; year } ; 1994 }
select the rows whose location record fuzzily matches to penang , malaysia . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; penang , malaysia } } ; 2 }
the sum of the capacity record of all rows is 185080 .
Output: round_eq { sum { all_rows ; capacity } ; 185080 }
select the rows whose away team score record is less than 10 . for the crowd records of these rows , all of them are greater than 17000 .
Output: all_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 17000 }
select the rows whose directed by record fuzzily matches to mel damski . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; directed by ; mel damski } } ; 4 }
select the rows whose builder record fuzzily matches to general dynamics , quincy . among these rows , select the rows whose commissioned - decommissioned record fuzzily matches to 1969 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; builder ; general dynamics , quincy } ; commissioned - decommissioned ; 1969 } } ; 2 }
select the rows whose height ( ft ) record is greater than or equal to 800 . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { all_rows ; height ( ft ) ; 800 } } ; 2 }
select the row whose attendance record of all rows is maximum . the opponent record of this row is melbourne storm .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; melbourne storm }
select the rows whose city record fuzzily matches to kelowna . there is only one such row in the table . the skip record of this unqiue row is bob ursel .
Output: and { only { filter_eq { all_rows ; city ; kelowna } } ; eq { hop { filter_eq { all_rows ; city ; kelowna } ; skip } ; bob ursel } }
select the rows whose location record fuzzily matches to tokyo , japan . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; tokyo , japan } } ; 3 }
select the rows whose bank record fuzzily matches to mellat bank . take the total assets ( score ) record of this row . select the rows whose bank record fuzzily matches to bank of industry and mine . take the total assets ( score ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; bank ; mellat bank } ; total assets ( score ) } ; hop { filter_eq { all_rows ; bank ; bank of industry and mine } ; total assets ( score ) } }
select the row whose points record of all rows is maximum . the driver record of this row is bruno junqueira .
Output: eq { hop { argmax { all_rows ; points } ; driver } ; bruno junqueira }
the 3rd maximum silver record of all rows is 42 . the rank record of the row with 3rd maximum silver record is 2 .
Output: and { eq { nth_max { all_rows ; silver ; 3 } ; 42 } ; eq { hop { nth_argmax { all_rows ; silver ; 3 } ; rank } ; 2 } }
the average of the weeks on top record of all rows is 3 .
Output: round_eq { avg { all_rows ; weeks on top } ; 3 }
select the rows whose position record fuzzily matches to ol . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; ol } } ; 3 }
for the designer , year records of all rows , most of them are less than 1950 .
Output: most_less { all_rows ; designer , year ; 1950 }
select the rows whose content record fuzzily matches to general television . for the language records of these rows , all of them fuzzily match to italian .
Output: all_eq { filter_eq { all_rows ; content ; general television } ; language ; italian }
select the row whose bronze record of all rows is 3rd maximum . the rank record of this row is 2 .
Output: eq { hop { nth_argmax { all_rows ; bronze ; 3 } ; rank } ; 2 }
select the rows whose outcome record fuzzily matches to runner - up . there is only one such row in the table .
Output: only { filter_eq { all_rows ; outcome ; runner - up } }
the average of the viewing figure record of all rows is 6.73 million .
Output: round_eq { avg { all_rows ; viewing figure } ; 6.73 million }
select the row whose attendance record of all rows is 3rd maximum . the date record of this row is 8 december 1999 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; 8 december 1999 }
select the rows whose player record fuzzily matches to antwaan randle el . take the yards record of this row . select the rows whose player record fuzzily matches to ladell betts . take the yards record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; antwaan randle el } ; yards } ; hop { filter_eq { all_rows ; player ; ladell betts } ; yards } }
select the row whose away team score record of all rows is maximum . the away team record of this row is essendon .
Output: eq { hop { argmax { all_rows ; away team score } ; away team } ; essendon }
the sum of the swedish championship titles record of all rows is 81 .
Output: round_eq { sum { all_rows ; swedish championship titles } ; 81 }
the average of the stations served record of all rows is 17.5 .
Output: round_eq { avg { all_rows ; stations served } ; 17.5 }
the average of the attendance record of all rows is 45557.14 .
Output: round_eq { avg { all_rows ; attendance } ; 45557.14 }
select the row whose population record of all rows is maximum . the official name record of this row is saint george .
Output: eq { hop { argmax { all_rows ; population } ; official name } ; saint george }
select the rows whose power rpm record is equal to 6800 . there is only one such row in the table . the model record of this unqiue row is sl 63 amg .
Output: and { only { filter_eq { all_rows ; power rpm ; 6800 } } ; eq { hop { filter_eq { all_rows ; power rpm ; 6800 } ; model } ; sl 63 amg } }
for the country records of all rows , most of them fuzzily match to belgium .
Output: most_eq { all_rows ; country ; belgium }
the sum of the attendance record of all rows is 779652 .
Output: round_eq { sum { all_rows ; attendance } ; 779652 }
select the row whose sales record of all rows is maximum . the title record of this row is m .
Output: eq { hop { argmax { all_rows ; sales } ; title } ; m }
select the rows whose country record fuzzily matches to australia . take the time record of this row . select the rows whose country record fuzzily matches to united states . take the time record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; country ; australia } ; time } ; hop { filter_eq { all_rows ; country ; united states } ; time } }
select the row whose commissioned record of all rows is 2nd minimum . the name record of this row is ledbury .
Output: eq { hop { nth_argmin { all_rows ; commissioned ; 2 } ; name } ; ledbury }
select the rows whose college record fuzzily matches to arizona . there is only one such row in the table . the player record of this unqiue row is jerome gladney .
Output: and { only { filter_eq { all_rows ; college ; arizona } } ; eq { hop { filter_eq { all_rows ; college ; arizona } ; player } ; jerome gladney } }
the average of the crowd record of all rows is 21000-22000 .
Output: round_eq { avg { all_rows ; crowd } ; 21000-22000 }
for the 2nd place records of all rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { all_rows ; 2nd place ; 1 }
the average of the points ( pts ) record of all rows is 24.6 .
Output: round_eq { avg { all_rows ; points ( pts ) } ; 24.6 }
for the time records of all rows , most of them are less than 7:00:00 .
Output: most_less { all_rows ; time ; 7:00:00 }
for the competition or tour records of all rows , most of them fuzzily match to friendly .
Output: most_eq { all_rows ; competition or tour ; friendly }
select the rows whose county team record fuzzily matches to kilkenny . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; county team ; kilkenny } } ; 4 }
for the decision records of all rows , most of them fuzzily match to backstrom .
Output: most_eq { all_rows ; decision ; backstrom }
for the date records of all rows , most of them are greater than 1930 .
Output: most_greater { all_rows ; date ; 1930 }
select the rows whose driver record fuzzily matches to sarah fisher . take the laps record of this row . select the rows whose driver record fuzzily matches to dan wheldon . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; driver ; sarah fisher } ; laps } ; hop { filter_eq { all_rows ; driver ; dan wheldon } ; laps } }
for the origin of programming records of all rows , most of them fuzzily match to india .
Output: most_eq { all_rows ; origin of programming ; india }
select the rows whose surface record fuzzily matches to grass . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; surface ; grass } } ; 3 }
select the rows whose performer 4 record fuzzily matches to john bird . there is only one such row in the table . the episode record of this unqiue row is 5 .
Output: and { only { filter_eq { all_rows ; performer 4 ; john bird } } ; eq { hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode } ; 5 } }
the average of the overall nt points record of all rows is 375.6 .
Output: round_eq { avg { all_rows ; overall nt points } ; 375.6 }
the average of the total record of all rows is 51 .
Output: round_eq { avg { all_rows ; total } ; 51 }
select the row whose time ( et ) record of all rows is maximum . the week record of this row is 9 . the opponent record of this row is pittsburgh steelers .
Output: and { eq { hop { argmax { all_rows ; time ( et ) } ; week } ; 9 } ; eq { hop { argmax { all_rows ; time ( et ) } ; opponent } ; pittsburgh steelers } }
select the rows whose championship record fuzzily matches to dayton , oh . there is only one such row in the table . the date record of this unqiue row is 8 february 1976 .
Output: and { only { filter_eq { all_rows ; championship ; dayton , oh } } ; eq { hop { filter_eq { all_rows ; championship ; dayton , oh } ; date } ; 8 february 1976 } }
select the row whose location attendance record of all rows is maximum . the date record of this row is february 21 .
Output: eq { hop { argmax { all_rows ; location attendance } ; date } ; february 21 }
select the rows whose date record fuzzily matches to 2000 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; 2000 } } ; 2 }
select the rows whose focal plane in ft ( m ) record fuzzily matches to n / a . there is only one such row in the table . the lighthouse record of this unqiue row is basco .
Output: and { only { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } } ; eq { hop { filter_eq { all_rows ; focal plane in ft ( m ) ; n / a } ; lighthouse } ; basco } }
select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row . select the rows whose opponent record fuzzily matches to st louis cardinals . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } ; hop { filter_eq { all_rows ; opponent ; st louis cardinals } ; date } }
select the rows whose type record fuzzily matches to trade . there is only one such row in the table . the name record of this unqiue row is pelé .
Output: and { only { filter_eq { all_rows ; type ; trade } } ; eq { hop { filter_eq { all_rows ; type ; trade } ; name } ; pelé } }
select the row whose from record of all rows is 1st minimum . the player record of this row is jim pollard .
Output: eq { hop { nth_argmin { all_rows ; from ; 1 } ; player } ; jim pollard }
select the rows whose venue record fuzzily matches to waverley park . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; venue ; waverley park } } ; 4 }
select the rows whose song record fuzzily matches to endless love . take the weeks on top record of this row . select the rows whose song record fuzzily matches to morning train . take the weeks on top record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; song ; endless love } ; weeks on top } ; hop { filter_eq { all_rows ; song ; morning train } ; weeks on top } }
the average of the candidates record of all rows is 65 % .
Output: round_eq { avg { all_rows ; candidates } ; 65 % }
select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk . there is only one such row in the table . the episode title record of this unqiue row is all the world 's a stage .
Output: and { only { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } } ; eq { hop { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } ; episode title } ; all the world 's a stage } }
select the rows whose previous conference record fuzzily matches to three rivers . there is only one such row in the table . the school record of this unqiue row is caston .
Output: and { only { filter_eq { all_rows ; previous conference ; three rivers } } ; eq { hop { filter_eq { all_rows ; previous conference ; three rivers } ; school } ; caston } }
select the rows whose population ( 2013 ) record is less than 2,000,000 . there is only one such row in the table . the province record of this unqiue row is northern cape .
Output: and { only { filter_less { all_rows ; population ( 2013 ) ; 2,000,000 } } ; eq { hop { filter_less { all_rows ; population ( 2013 ) ; 2,000,000 } ; province } ; northern cape } }
for the attendance records of all rows , most of them are greater than 20000 .
Output: most_greater { all_rows ; attendance ; 20000 }
select the rows whose visitor record fuzzily matches to carolina . among these rows , select the rows whose attendance record is greater than 19000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; visitor ; carolina } ; attendance ; 19000 } } ; 3 }
select the row whose date record of all rows is 17th minimum . the competition record of this row is 2012 philippine peace cup .
Output: eq { hop { nth_argmin { all_rows ; date ; 17 } ; competition } ; 2012 philippine peace cup }
select the rows whose trainer record fuzzily matches to d wayne lukas . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; trainer ; d wayne lukas } } ; 2 }
select the rows whose manner of departure record fuzzily matches to signed by fc fredericia . there is only one such row in the table . the team record of this unqiue row is fc hjørring .
Output: and { only { filter_eq { all_rows ; manner of departure ; signed by fc fredericia } } ; eq { hop { filter_eq { all_rows ; manner of departure ; signed by fc fredericia } ; team } ; fc hjørring } }
select the row whose location / attendance record of all rows is maximum . the date record of this row is september 5 .
Output: eq { hop { argmax { all_rows ; location / attendance } ; date } ; september 5 }
select the rows whose partner record fuzzily matches to margaret court . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; partner ; margaret court } } ; 6 }
for the broadcast date records of all rows , most of them fuzzily match to august .
Output: most_eq { all_rows ; broadcast date ; august }
select the row whose original air date record of all rows is 2nd maximum . the production code record of this row is 407 .
Output: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; production code } ; 407 }
select the rows whose team record fuzzily matches to america . the average of the average fencers rank record of these rows is 6.58 .
Output: round_eq { avg { filter_eq { all_rows ; team ; america } ; average fencers rank } ; 6.58 }
select the rows whose venue record fuzzily matches to new delhi . the 1st maximum date record of these rows is 22 august 2009 .
Output: eq { nth_max { filter_eq { all_rows ; venue ; new delhi } ; date ; 1 } ; 22 august 2009 }
select the rows whose player record fuzzily matches to sam torrance . take the to par record of this row . select the rows whose player record fuzzily matches to ben crenshaw . take the to par record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; sam torrance } ; to par } ; hop { filter_eq { all_rows ; player ; ben crenshaw } ; to par } }
for the shot pct records of all rows , most of them are greater than 65 % .
Output: most_greater { all_rows ; shot pct ; 65 % }
select the rows whose result record fuzzily matches to none . there is only one such row in the table . the week record of this unqiue row is 3 .
Output: and { only { filter_eq { all_rows ; result ; none } } ; eq { hop { filter_eq { all_rows ; result ; none } ; week } ; 3 } }
select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 .
Output: and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } }
select the rows whose gross record is greater than 300000000 . there is only one such row in the table . the title record of this unqiue row is fatal attraction .
Output: and { only { filter_greater { all_rows ; gross ; 300000000 } } ; eq { hop { filter_greater { all_rows ; gross ; 300000000 } ; title } ; fatal attraction } }
select the rows whose successor record fuzzily matches to hopkins holsey ( j ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to john young ( aj ) . take the date successor seated record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; successor ; hopkins holsey ( j ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; john young ( aj ) } ; date successor seated } }
select the rows whose status record fuzzily matches to bottom 3 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; status ; bottom 3 } } ; 2 }
select the rows whose incumbent record fuzzily matches to matthew clay . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john w eppes . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; matthew clay } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john w eppes } ; first elected } }
select the rows whose silver record is greater than 0 . among these rows , select the rows whose gold record is equal to 4 . there is only one such row in the table . the athlete record of this unqiue row is mohammad nassiri .
Output: and { only { filter_eq { filter_greater { all_rows ; silver ; 0 } ; gold ; 4 } } ; eq { hop { filter_eq { filter_greater { all_rows ; silver ; 0 } ; gold ; 4 } ; athlete } ; mohammad nassiri } }
select the rows whose location attendance record fuzzily matches to amway arena . among these rows , select the rows whose high points record fuzzily matches to andre iguodala . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location attendance ; amway arena } ; high points ; andre iguodala } } ; 2 }
select the rows whose venue record fuzzily matches to carver arena . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; venue ; carver arena } } ; 3 }
select the rows whose laps record is equal to 64 . for the points records of these rows , most of them are greater than or equal to 15 .
Output: most_greater_eq { filter_eq { all_rows ; laps ; 64 } ; points ; 15 }
select the rows whose hosted 4 teams since record is equal to 1993 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; hosted 4 teams since ; 1993 } } ; 2 }
select the rows whose competition record fuzzily matches to friendly . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
Output: and { only { filter_eq { all_rows ; competition ; friendly } } ; eq { hop { filter_eq { all_rows ; competition ; friendly } ; date } ; 15 august 2012 } }
for the population density ( per km square ) records of all rows , all of them are greater than 100 .
Output: all_greater { all_rows ; population density ( per km square ) ; 100 }
select the rows whose city record fuzzily matches to jeddah . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; city ; jeddah } } ; 7 }
select the rows whose nickname record fuzzily matches to bulldogs . there is only one such row in the table . the institution record of this unqiue row is georgia military college .
Output: and { only { filter_eq { all_rows ; nickname ; bulldogs } } ; eq { hop { filter_eq { all_rows ; nickname ; bulldogs } ; institution } ; georgia military college } }
select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to center . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; center } } ; 2 }
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 .
Output: eq { diff { hop { filter_eq { all_rows ; singer ; pedro miguéis } ; points } ; hop { filter_eq { all_rows ; singer ; joão portugal } ; points } } ; 20 }
select the rows whose result record fuzzily matches to re-elected . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; result ; re-elected } } ; 5 }
select the row whose attendance record of all rows is maximum . the date record of this row is november 14 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; november 14 }
the average of the to par record of all rows is -3.4 .
Output: round_eq { avg { all_rows ; to par } ; -3.4 }
the average of the top - 10 record of all rows is 5-6 .
Output: round_eq { avg { all_rows ; top - 10 } ; 5-6 }
select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the english title record of this unqiue row is bad education .
Output: and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; english title } ; bad education } }
select the rows whose high points record fuzzily matches to jason richardson . the average of the high points record of these rows is 31 .
Output: round_eq { avg { filter_eq { all_rows ; high points ; jason richardson } ; high points } ; 31 }
select the rows whose publisher record fuzzily matches to bethesda softworks . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; publisher ; bethesda softworks } } ; 2 }
select the row whose years for grizzlies record of all rows is minimum . the player record of this row is pete chilcutt .
Output: eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; pete chilcutt }
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 .
Output: less { hop { filter_eq { all_rows ; name ; erna hanfstaengl } ; life } ; hop { filter_eq { all_rows ; name ; maria reiter } ; life } }
select the rows whose game site record fuzzily matches to kingdome . among these rows , select the rows whose attendance record is less than 62000 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_eq { all_rows ; game site ; kingdome } ; attendance ; 62000 } } ; 3 }
the average of the crowd record of all rows is 24248 .
Output: round_eq { avg { all_rows ; crowd } ; 24248 }
select the rows whose fsb record fuzzily matches to 100 mt/s . for the mult records of these rows , all of them are greater than 4 .
Output: all_greater { filter_eq { all_rows ; fsb ; 100 mt/s } ; mult ; 4 }
select the rows whose td 's record is equal to 12 . there is only one such row in the table . the player record of this unqiue row is jerome riley .
Output: and { only { filter_eq { all_rows ; td 's ; 12 } } ; eq { hop { filter_eq { all_rows ; td 's ; 12 } ; player } ; jerome riley } }
select the row whose worldwide gross record of all rows is 3rd maximum . the title record of this row is mission : impossible .
Output: eq { hop { nth_argmax { all_rows ; worldwide gross ; 3 } ; title } ; mission : impossible }
select the row whose area km 2 record of all rows is maximum . the official name record of this row is kent .
Output: eq { hop { argmax { all_rows ; area km 2 } ; official name } ; kent }
for the location records of all rows , most of them fuzzily match to edison international field of anaheim .
Output: most_eq { all_rows ; location ; edison international field of anaheim }
the average of the mark record of all rows is 47.05 .
Output: round_eq { avg { all_rows ; mark } ; 47.05 }
the average of the viewers record of all rows is 44 .
Output: round_eq { avg { all_rows ; viewers } ; 44 }
select the rows whose object type record fuzzily matches to open cluster . select the row whose apparent magnitude record of these rows is 2nd maximum . the constellation record of this row is hydra .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; object type ; open cluster } ; apparent magnitude ; 2 } ; constellation } ; hydra }
select the rows whose date record fuzzily matches to october . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; date ; october } } ; 8 }
select the rows whose home team record fuzzily matches to fitzroy . 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 .
Output: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } }
select the rows whose language record fuzzily matches to portuguese . take the points record of this row . select the rows whose language record fuzzily matches to spanish . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; language ; portuguese } ; points } ; hop { filter_eq { all_rows ; language ; spanish } ; points } }
select the rows whose date record fuzzily matches to october . select the row whose date record of these rows is 1st minimum . the opponent record of this row is pittsburgh steelers .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; date ; 1 } ; opponent } ; pittsburgh steelers }
select the rows whose remixed by record fuzzily matches to laurent boutonnat . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; remixed by ; laurent boutonnat } } ; 2 }
select the rows whose apps record is equal to 37 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; apps ; 37 } } ; 2 }
for the visitor records of all rows , most of them fuzzily match to warriors .
Output: most_eq { all_rows ; visitor ; warriors }
select the row whose number record of all rows is maximum . the language record of this row is polish .
Output: eq { hop { argmax { all_rows ; number } ; language } ; polish }
select the rows whose contributions record is less than 5000000 . there is only one such row in the table . the candidate record of this unqiue row is dennis kucinich .
Output: and { only { filter_less { all_rows ; contributions ; 5000000 } } ; eq { hop { filter_less { all_rows ; contributions ; 5000000 } ; candidate } ; dennis kucinich } }
select the row whose against record of all rows is maximum . the opposing teams record of this row is argentina .
Output: eq { hop { argmax { all_rows ; against } ; opposing teams } ; argentina }
select the rows whose wins record is equal to 0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; wins ; 0 } } ; 3 }
the sum of the enrollment record of all rows is 44185 .
Output: round_eq { sum { all_rows ; enrollment } ; 44185 }
select the rows whose location record fuzzily matches to three rivers stadium . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; location ; three rivers stadium } } ; 8 }
select the rows whose webcast record is not equal to - . the average of the frequency record of these rows is 958 .
Output: round_eq { avg { filter_not_eq { all_rows ; webcast ; - } ; frequency } ; 958 }
select the rows whose deleted record is not equal to - . there is only one such row in the table . the name record of this unqiue row is mowbray engineering .
Output: and { only { filter_not_eq { all_rows ; deleted ; - } } ; eq { hop { filter_not_eq { all_rows ; deleted ; - } ; name } ; mowbray engineering } }
select the rows whose nationality record fuzzily matches to united states . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; nationality ; united states } } ; 8 }
select the rows whose goals record fuzzily matches to ( 1 ) . there is only one such row in the table . the player record of this unqiue row is dario vidošić .
Output: and { only { filter_eq { all_rows ; goals ; ( 1 ) } } ; eq { hop { filter_eq { all_rows ; goals ; ( 1 ) } ; player } ; dario vidošić } }
select the rows whose tournament record fuzzily matches to egypt . there is only one such row in the table . the date record of this unqiue row is 31 october 2010 .
Output: and { only { filter_eq { all_rows ; tournament ; egypt } } ; eq { hop { filter_eq { all_rows ; tournament ; egypt } ; date } ; 31 october 2010 } }
the average of the seats won record of all rows is 112 .
Output: round_eq { avg { all_rows ; seats won } ; 112 }
select the rows whose party record does not match to totals . the number of such rows is 7 .
Output: eq { count { filter_not_eq { all_rows ; party ; totals } } ; 7 }
select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon . the away team record of this unqiue row is st kilda .
Output: and { only { filter_eq { all_rows ; venue ; windy hill } } ; and { eq { hop { filter_eq { all_rows ; venue ; windy hill } ; home team } ; essendon } ; eq { hop { filter_eq { all_rows ; venue ; windy hill } ; away team } ; st kilda } } }
select the row whose overall record record of all rows is maximum . the school record of this row is delmar .
Output: eq { hop { argmax { all_rows ; overall record } ; school } ; delmar }
select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to nbc . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; nbc } } ; 4 }
select the rows whose player record fuzzily matches to hubert green . take the to par record of this row . select the rows whose player record fuzzily matches to dave stockton . take the to par record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; hubert green } ; to par } ; hop { filter_eq { all_rows ; player ; dave stockton } ; to par } }
for the memory bandwidth ( gb / s ) records of all rows , most of them are greater than or equal to 17.1 .
Output: most_greater_eq { all_rows ; memory bandwidth ( gb / s ) ; 17.1 }
select the rows whose became duke record fuzzily matches to uncles death . there is only one such row in the table . the name record of this unqiue row is louis antoine de rohan - chabot .
Output: and { only { filter_eq { all_rows ; became duke ; uncles death } } ; eq { hop { filter_eq { all_rows ; became duke ; uncles death } ; name } ; louis antoine de rohan - chabot } }
for the nature of incident records of all rows , most of them fuzzily match to hostile .
Output: most_eq { all_rows ; nature of incident ; hostile }
select the rows whose notes record fuzzily matches to not published in book form . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; notes ; not published in book form } } ; 9 }
select the rows whose team record fuzzily matches to team australia . the average of the best record of these rows is 50.12 .
Output: round_eq { avg { filter_eq { all_rows ; team ; team australia } ; best } ; 50.12 }
select the row whose total points record of all rows is maximum . the player record of this row is carlos yates .
Output: eq { hop { argmax { all_rows ; total points } ; player } ; carlos yates }
select the rows whose event record fuzzily matches to wc rio de janeiro . the maximum rank points record of these rows is 10 . the shooter record of the row with superlative rank points record is leuris pupo ( cub ) .
Output: and { eq { max { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; 10 } ; eq { hop { argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; shooter } ; leuris pupo ( cub ) } }
select the row whose location attendance record of all rows is 1st maximum . the team record of this row is washington .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; washington }
select the rows whose location record fuzzily matches to united states . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; location ; united states } } ; 7 }
select the rows whose outgoing manager record fuzzily matches to mesut bakkal . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to samet aybaba . take the date of vacancy record of this row . the second record is 1 day larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; outgoing manager ; mesut bakkal } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; samet aybaba } ; date of vacancy } } ; -1 day }
select the rows whose location record fuzzily matches to virginia beach . there is only one such row in the table . the goal record of this unqiue row is 2 .
Output: and { only { filter_eq { all_rows ; location ; virginia beach } } ; eq { hop { filter_eq { all_rows ; location ; virginia beach } ; goal } ; 2 } }
select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % . there is only one such row in the table . the currency record of this unqiue row is yen . the country record of this unqiue row is japan .
Output: and { only { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } } ; and { eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen } ; eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan } } }
select the rows whose year record is less than 2000 . the sum of the starts record of these rows is 28 .
Output: round_eq { sum { filter_less { all_rows ; year ; 2000 } ; starts } ; 28 }
select the rows whose home team score record is greater than 12 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_greater { all_rows ; home team score ; 12 } ; crowd ; 20000 } } ; 2 }
select the row whose overall record of all rows is 2nd minimum . the name record of this row is charley holm .
Output: eq { hop { nth_argmin { all_rows ; overall ; 2 } ; name } ; charley holm }
for the date records of all rows , all of them fuzzily match to 11 april 1981 .
Output: all_eq { all_rows ; date ; 11 april 1981 }
select the row whose production num record of all rows is maximum . the title record of this row is bosko 's fox hunt .
Output: eq { hop { argmax { all_rows ; production num } ; title } ; bosko 's fox hunt }
select the row whose silver record of all rows is 2nd maximum . the province record of this row is chonburi .
Output: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; province } ; chonburi }
select the row whose registered voters record of all rows is 3rd maximum . the city record of this row is encinitas .
Output: eq { hop { nth_argmax { all_rows ; registered voters ; 3 } ; city } ; encinitas }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is frank wolf .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; frank wolf }
select the rows whose format record fuzzily matches to limited edition . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; format ; limited edition } } ; 5 }
select the rows whose year record fuzzily matches to 1996 . take the laps record of this row . select the rows whose year record fuzzily matches to 1997 . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1996 } ; laps } ; hop { filter_eq { all_rows ; year ; 1997 } ; laps } }
select the rows whose engine record fuzzily matches to 16 v . for the power records of these rows , all of them are greater than or equal to at4000 rpm .
Output: all_greater_eq { filter_eq { all_rows ; engine ; 16 v } ; power ; at4000 rpm }
select the rows whose date record fuzzily matches to april 2 . take the high points record of this row . select the rows whose date record fuzzily matches to april 15 . take the high points record of this row . the first record does not match to the second record .
Output: not_eq { hop { filter_eq { all_rows ; date ; april 2 } ; high points } ; hop { filter_eq { all_rows ; date ; april 15 } ; high points } }
select the rows whose team record fuzzily matches to donlavey . there is only one such row in the table . the year record of this unqiue row is 1990 .
Output: and { only { filter_eq { all_rows ; team ; donlavey } } ; eq { hop { filter_eq { all_rows ; team ; donlavey } ; year } ; 1990 } }