niryuu/sni-each-converted huggingface dataset:
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is may 24 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; may 24 }
the average of the time record of all rows is 6:21.73 .
Output: round_eq { avg { all_rows ; time } ; 6:21.73 }
select the rows whose year of publication record fuzzily matches to 2007 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; year of publication ; 2007 } } ; 4 }
the 2nd minimum year record of all rows is 1980 .
Output: eq { nth_min { all_rows ; year ; 2 } ; 1980 }
the average of the score record of all rows is 210.6 .
Output: round_eq { avg { all_rows ; score } ; 210.6 }
select the rows whose language form record fuzzily matches to nynorsk . select the row whose area record of these rows is 2nd maximum . the name record of this row is samnanger .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; language form ; nynorsk } ; area ; 2 } ; name } ; samnanger }
select the rows whose position record fuzzily matches to end . among these rows , select the rows whose draft record fuzzily matches to nfl . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; position ; end } ; draft ; nfl } } ; 2 }
select the rows whose content record fuzzily matches to salute . there is only one such row in the table . the television service record of this unqiue row is erde und mensch .
Output: and { only { filter_eq { all_rows ; content ; salute } } ; eq { hop { filter_eq { all_rows ; content ; salute } ; television service } ; erde und mensch } }
for the primary sponsor ( s ) records of all rows , most of them do not match to n / a .
Output: most_not_eq { all_rows ; primary sponsor ( s ) ; n / a }
select the rows whose entrant record fuzzily matches to jaguar racing . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; entrant ; jaguar racing } } ; 3 }
for the mens singles records of all rows , most of them fuzzily match to lee chong wei .
Output: most_eq { all_rows ; mens singles ; lee chong wei }
the 3rd minimum round record of all rows is 3 . the name record of the row with 3rd minimum round record is scott starks .
Output: and { eq { nth_min { all_rows ; round ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; round ; 3 } ; name } ; scott starks } }
select the row whose goals scored record of all rows is maximum . the club record of this row is fbk kaunas .
Output: eq { hop { argmax { all_rows ; goals scored } ; club } ; fbk kaunas }
select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany . there is only one such row in the table . the date record of this unqiue row is july 17 , 2011 .
Output: and { only { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } } ; eq { hop { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } ; date } ; july 17 , 2011 } }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is finis j garrett .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; finis j garrett }
select the rows whose name record fuzzily matches to kotokasuga . take the top division debut record of this row . select the rows whose name record fuzzily matches to yoshiazuma . take the top division debut record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; kotokasuga } ; top division debut } ; hop { filter_eq { all_rows ; name ; yoshiazuma } ; top division debut } }
select the rows whose best uk team record fuzzily matches to leeds university . take the year record of this row . select the rows whose best uk team record fuzzily matches to oxford brookes university . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; best uk team ; leeds university } ; year } ; hop { filter_eq { all_rows ; best uk team ; oxford brookes university } ; year } }
select the row whose first store record of all rows is 3rd minimum . the country record of this row is spain .
Output: eq { hop { nth_argmin { all_rows ; first store ; 3 } ; country } ; spain }
select the rows whose deceased spouse record fuzzily matches to jessica mitford . take the length of marriage record of this row . select the rows whose deceased spouse record fuzzily matches to helen palmer geisel . take the length of marriage record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; deceased spouse ; jessica mitford } ; length of marriage } ; hop { filter_eq { all_rows ; deceased spouse ; helen palmer geisel } ; length of marriage } }
select the row whose enrollment record of all rows is 2nd minimum . the institution record of this row is north georgia technical college .
Output: eq { hop { nth_argmin { all_rows ; enrollment ; 2 } ; institution } ; north georgia technical college }
select the rows whose chassis record fuzzily matches to milano speluzzi . there is only one such row in the table . the entrant record of this unqiue row is scuderia milano .
Output: and { only { filter_eq { all_rows ; chassis ; milano speluzzi } } ; eq { hop { filter_eq { all_rows ; chassis ; milano speluzzi } ; entrant } ; scuderia milano } }
select the rows whose tournament record fuzzily matches to 2009 african youth championship ( qualifiers ) . among these rows , select the rows whose home team record fuzzily matches to zambia . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; tournament ; 2009 african youth championship ( qualifiers ) } ; home team ; zambia } } ; 2 }
the average of the attendance record of all rows is 60644 .
Output: round_eq { avg { all_rows ; attendance } ; 60644 }
select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table . the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .
Output: and { only { filter_eq { all_rows ; object type ; irregular galaxy } } ; and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } } }
select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; aspect ; 4:3 } } ; 7 }
select the rows whose locale record fuzzily matches to prince edward island . there is only one such row in the table . the skip record of this unqiue row is rod macdonald .
Output: and { only { filter_eq { all_rows ; locale ; prince edward island } } ; eq { hop { filter_eq { all_rows ; locale ; prince edward island } ; skip } ; rod macdonald } }
select the row whose enrollment record of all rows is 2nd maximum . the institution record of this row is minot state university .
Output: eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; institution } ; minot state university }
select the rows whose college record fuzzily matches to acadia . there is only one such row in the table . the player record of this unqiue row is karl ortmanns .
Output: and { only { filter_eq { all_rows ; college ; acadia } } ; eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns } }
select the rows whose to par record is equal to -1 . there is only one such row in the table . the player record of this unqiue row is jodie mudd .
Output: and { only { filter_eq { all_rows ; to par ; -1 } } ; eq { hop { filter_eq { all_rows ; to par ; -1 } ; player } ; jodie mudd } }
for the supplier records of all rows , most of them fuzzily match to kooga .
Output: most_eq { all_rows ; supplier ; kooga }
select the row whose matches record of all rows is maximum . the goalkeeper record of this row is asier riesgo .
Output: eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; asier riesgo }
select the rows whose venue record fuzzily matches to colombo . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; venue ; colombo } } ; 2 }
the average of the attendance record of all rows is 46,203 .
Output: round_eq { avg { all_rows ; attendance } ; 46,203 }
the minimum place record of all rows is 1 . the player record of the row with superlative place record is trevor immelman .
Output: and { eq { min { all_rows ; place } ; 1 } ; eq { hop { argmin { all_rows ; place } ; player } ; trevor immelman } }
select the row whose released record of all rows is 2nd minimum . the codename record of this row is sb700 .
Output: eq { hop { nth_argmin { all_rows ; released ; 2 } ; codename } ; sb700 }
select the rows whose surface record fuzzily matches to hard . the maximum date record of these rows is august 4 , 2013 .
Output: eq { max { filter_eq { all_rows ; surface ; hard } ; date } ; august 4 , 2013 }
the average of the overall record record of all rows is 5 .
Output: round_eq { avg { all_rows ; overall record } ; 5 }
select the rows whose main town record fuzzily matches to adampan . take the population density ( / km 2 ) record of this row . select the rows whose main town record fuzzily matches to chilawathurai . take the population density ( / km 2 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; main town ; adampan } ; population density ( / km 2 ) } ; hop { filter_eq { all_rows ; main town ; chilawathurai } ; population density ( / km 2 ) } }
select the rows whose seats contested record is greater than 40 . select the row whose no of votes record of these rows is minimum . the party record of this row is bharatiya janata party .
Output: eq { hop { argmin { filter_greater { all_rows ; seats contested ; 40 } ; no of votes } ; party } ; bharatiya janata party }
select the rows whose year record fuzzily matches to 2002 . take the tournaments played record of this row . select the rows whose year record fuzzily matches to 2003 . take the tournaments played record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2002 } ; tournaments played } ; hop { filter_eq { all_rows ; year ; 2003 } ; tournaments played } }
for the area ( km square ) records of all rows , most of them are greater than 40 .
Output: most_greater { all_rows ; area ( km square ) ; 40 }
select the rows whose year record is greater than or equal to 1990 . the number of such rows is 4 .
Output: eq { count { filter_greater_eq { all_rows ; year ; 1990 } } ; 4 }
for the transfer fee records of all rows , most of them fuzzily match to free .
Output: most_eq { all_rows ; transfer fee ; free }
for the platform ( s ) records of all rows , most of them fuzzily match to windows .
Output: most_eq { all_rows ; platform ( s ) ; windows }
select the rows whose position record fuzzily matches to forward-center . there is only one such row in the table . the player record of this unqiue row is keon clark .
Output: and { only { filter_eq { all_rows ; position ; forward-center } } ; eq { hop { filter_eq { all_rows ; position ; forward-center } ; player } ; keon clark } }
select the rows whose date record fuzzily matches to april . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; date ; april } } ; 5 }
select the rows whose nationality record fuzzily matches to china . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nationality ; china } } ; 2 }
select the rows whose designation hd record fuzzily matches to hd 178428 . take the arrival date record of this row . select the rows whose designation hd record fuzzily matches to hd 186408 . take the arrival date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; designation hd ; hd 178428 } ; arrival date } ; hop { filter_eq { all_rows ; designation hd ; hd 186408 } ; arrival date } }
select the row whose attendance record of all rows is maximum . the venue record of this row is gosford .
Output: eq { hop { argmax { all_rows ; attendance } ; venue } ; gosford }
select the rows whose nationality record fuzzily matches to jamaica . select the row whose reaction record of these rows is 2nd minimum . the name record of this row is brigitte foster - hylton .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; nationality ; jamaica } ; reaction ; 2 } ; name } ; brigitte foster - hylton }
select the row whose location attendance record of all rows is maximum . the date record of this row is november 3 .
Output: eq { hop { argmax { all_rows ; location attendance } ; date } ; november 3 }
select the row whose inn record of all rows is 2nd minimum . the team record of this row is arizona diamondbacks .
Output: eq { hop { nth_argmin { all_rows ; inn ; 2 } ; team } ; arizona diamondbacks }
the sum of the total record of all rows is 1,353 .
Output: round_eq { sum { all_rows ; total } ; 1,353 }
select the rows whose segment a record fuzzily matches to metal detectors . take the episode record of this row . select the rows whose segment a record fuzzily matches to riding mowers . take the episode record of this row . the second record is 1 larger than the first record . the episode record of the first row is 151 . the episode record of the second row is 152 .
Output: and { eq { diff { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; segment a ; metal detectors } ; episode } ; 151 } ; eq { hop { filter_eq { all_rows ; segment a ; riding mowers } ; episode } ; 152 } } }
select the row whose year record of all rows is 2nd maximum . the womens singles record of this row is wang yihan .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; womens singles } ; wang yihan }
select the row whose in service for nal record of all rows is minimum . the ship record of this row is sskristianiafjord .
Output: eq { hop { argmin { all_rows ; in service for nal } ; ship } ; sskristianiafjord }
select the rows whose opponent record fuzzily matches to san francisco 49ers . among these rows , select the rows whose attendance record is equal to 39727 . there is only one such row in the table .
Output: only { filter_eq { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance ; 39727 } }
select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } } ; 3 }
select the row whose 2014 2 record of all rows is maximum . the greek national account record of this row is public debt 8 ( billion ) .
Output: eq { hop { argmax { all_rows ; 2014 2 } ; greek national account } ; public debt 8 ( billion ) }
select the rows whose college record fuzzily matches to adams state . there is only one such row in the table . the name record of this unqiue row is carl palazzo .
Output: and { only { filter_eq { all_rows ; college ; adams state } } ; eq { hop { filter_eq { all_rows ; college ; adams state } ; name } ; carl palazzo } }
select the rows whose artist record fuzzily matches to various artists ( compilation ) . for the date records of these rows , most of them fuzzily match to 1992 .
Output: most_eq { filter_eq { all_rows ; artist ; various artists ( compilation ) } ; date ; 1992 }
select the rows whose country record fuzzily matches to eng . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; country ; eng } } ; 11 }
for the remittances 2009 records of all rows , most of them are less than 23 .
Output: most_less { all_rows ; remittances 2009 ; 23 }
select the rows whose lap - by - lap record fuzzily matches to marty reid . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; lap - by - lap ; marty reid } } ; 4 }
select the rows whose type record fuzzily matches to end of loan . there is only one such row in the table . the name record of this unqiue row is bertolt .
Output: and { only { filter_eq { all_rows ; type ; end of loan } } ; eq { hop { filter_eq { all_rows ; type ; end of loan } ; name } ; bertolt } }
for the built records of all rows , most of them are less than 2000 .
Output: most_less { all_rows ; built ; 2000 }
the 1st minimum place record of all rows is 1 . the artist record of the row with 1st minimum place record is vnia fernandes .
Output: and { eq { nth_min { all_rows ; place ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; place ; 1 } ; artist } ; vnia fernandes } }
select the row whose season premiere record of all rows is 2nd maximum . the ranking record of this row is 67th .
Output: eq { hop { nth_argmax { all_rows ; season premiere ; 2 } ; ranking } ; 67th }
select the rows whose player record fuzzily matches to fred couples . take the weeks record of this row . select the rows whose player record fuzzily matches to david duval . take the weeks record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; fred couples } ; weeks } ; hop { filter_eq { all_rows ; player ; david duval } ; weeks } }
select the rows whose driver record fuzzily matches to kenny bräck . take the time / retired record of this row . select the rows whose driver record fuzzily matches to jimmy vasser . take the time / retired record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; driver ; kenny bräck } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jimmy vasser } ; time / retired } }
select the rows whose competition record fuzzily matches to friendly . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 3 }
the sum of the points record of all rows is 526 .
Output: round_eq { sum { all_rows ; points } ; 526 }
for the loser records of all rows , most of them fuzzily match to kansas city chiefs .
Output: most_eq { all_rows ; loser ; kansas city chiefs }
select the rows whose status record fuzzily matches to won . there is only one such row in the table . the year record of this unqiue row is 1957 .
Output: and { only { filter_eq { all_rows ; status ; won } } ; eq { hop { filter_eq { all_rows ; status ; won } ; year } ; 1957 } }
select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to st kilda . take the home team score record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; st kilda } ; home team score } }
for the origin records of all rows , most of them fuzzily match to pune railway station .
Output: most_eq { all_rows ; origin ; pune railway station }
the average of the home team score record of all rows is 17.67 .
Output: round_eq { avg { all_rows ; home team score } ; 17.67 }
select the rows whose td 's record is equal to 17 . there is only one such row in the table . the player name record of this unqiue row is torrance marshall .
Output: and { only { filter_eq { all_rows ; td 's ; 17 } } ; eq { hop { filter_eq { all_rows ; td 's ; 17 } ; player name } ; torrance marshall } }
select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to ezekiel c gathings . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; ezekiel c gathings } ; first elected } }
select the rows whose no in series record is greater than or equal to 40 . the sum of the us viewers ( millions ) record of these rows is 26.10 .
Output: round_eq { sum { filter_greater_eq { all_rows ; no in series ; 40 } ; us viewers ( millions ) } ; 26.10 }
select the rows whose model number record fuzzily matches to core i7 - 2649 m . take the frequency record of this row . select the rows whose model number record fuzzily matches to core i7 - 2629 m . take the frequency record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; model number ; core i7 - 2649 m } ; frequency } ; hop { filter_eq { all_rows ; model number ; core i7 - 2629 m } ; frequency } }
select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is -7.6 .
Output: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; -7.6 }
for the results records of all rows , all of them fuzzily match to re - elected .
Output: all_eq { all_rows ; results ; re - elected }
select the rows whose country record fuzzily matches to united states . the number of such rows is 12 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 12 }
the average of the population ( 2010 ) record of all rows is 17935 .
Output: round_eq { avg { all_rows ; population ( 2010 ) } ; 17935 }
select the row whose 2004 - 05 pts record of all rows is maximum . the team record of this row is vélez sársfield .
Output: eq { hop { argmax { all_rows ; 2004 - 05 pts } ; team } ; vélez sársfield }
select the row whose capacity record of all rows is 2nd minimum . the stadium record of this row is stadio italia .
Output: eq { hop { nth_argmin { all_rows ; capacity ; 2 } ; stadium } ; stadio italia }
select the rows whose election record fuzzily matches to 2004 . take the share of votes record of this row . select the rows whose election record fuzzily matches to 1996 . take the share of votes record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; election ; 2004 } ; share of votes } ; hop { filter_eq { all_rows ; election ; 1996 } ; share of votes } }
for the withdrawn records of all rows , most of them are equal to 1951 .
Output: most_eq { all_rows ; withdrawn ; 1951 }
select the rows whose wins record is equal to 13 . the average of the points for record of these rows is 1174 .
Output: round_eq { avg { filter_eq { all_rows ; wins ; 13 } ; points for } ; 1174 }
select the row whose weight ( lb ) record of all rows is maximum . the name record of this row is gary mcghee .
Output: eq { hop { argmax { all_rows ; weight ( lb ) } ; name } ; gary mcghee }
select the rows whose venue record fuzzily matches to away . the average of the attendance record of these rows is 565.33 .
Output: round_eq { avg { filter_eq { all_rows ; venue ; away } ; attendance } ; 565.33 }
select the rows whose year record is greater than or equal to 1960 . the maximum rank record of these rows is 17 .
Output: eq { max { filter_greater_eq { all_rows ; year ; 1960 } ; rank } ; 17 }
select the rows whose format record fuzzily matches to double cd . there is only one such row in the table . the region record of this unqiue row is scandinavia . the date record of this unqiue row is 2002 .
Output: and { only { filter_eq { all_rows ; format ; double cd } } ; and { eq { hop { filter_eq { all_rows ; format ; double cd } ; region } ; scandinavia } ; eq { hop { filter_eq { all_rows ; format ; double cd } ; date } ; 2002 } } }
select the rows whose title record fuzzily matches to big time babysitting . take the original us air date record of this row . select the rows whose title record fuzzily matches to big time gold . take the original us air date record of this row . the first record is less than the second record . the original us air date record of the first row is october 13 , 2012 . the original us air date record of the second row is october 20 , 2012 .
Output: and { less { hop { filter_eq { all_rows ; title ; big time babysitting } ; original us air date } ; hop { filter_eq { all_rows ; title ; big time gold } ; original us air date } } ; and { eq { hop { filter_eq { all_rows ; title ; big time babysitting } ; original us air date } ; october 13 , 2012 } ; eq { hop { filter_eq { all_rows ; title ; big time gold } ; original us air date } ; october 20 , 2012 } } }
select the rows whose album record fuzzily matches to come on over . take the weeks at number one record of this row . select the rows whose album record fuzzily matches to let 's talk about love . take the weeks at number one record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; album ; come on over } ; weeks at number one } ; hop { filter_eq { all_rows ; album ; let 's talk about love } ; weeks at number one } }
select the rows whose theme record fuzzily matches to contestant 's choice . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; theme ; contestant 's choice } } ; 3 }
select the row whose worldwide gross record of all rows is 4th maximum . the title record of this row is the hobbit : an unexpected journey .
Output: eq { hop { nth_argmax { all_rows ; worldwide gross ; 4 } ; title } ; the hobbit : an unexpected journey }
for the population records of all rows , most of them are greater than 200 .
Output: most_greater { all_rows ; population ; 200 }
select the rows whose nationality record fuzzily matches to canada . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 5 }
select the rows whose college / junior / club team ( league ) record fuzzily matches to northern michigan university ( ncaa ) . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; college / junior / club team ( league ) ; northern michigan university ( ncaa ) } } ; 2 }
select the rows whose nominee record fuzzily matches to anthony hopkins . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nominee ; anthony hopkins } } ; 2 }
select the row whose sack record of all rows is maximum . the player record of this row is craig walls .
Output: eq { hop { argmax { all_rows ; sack } ; player } ; craig walls }
select the rows whose pos record fuzzily matches to gk . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; pos ; gk } } ; 2 }
select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is lane evans .
Output: eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; lane evans }
select the rows whose winner record fuzzily matches to mario fazio . there is only one such row in the table .
Output: only { filter_eq { all_rows ; winner ; mario fazio } }
select the row whose torque ( nm ) / rpm record of all rows is maximum . the model / engine record of this row is 2.0 duratec he .
Output: eq { hop { argmax { all_rows ; torque ( nm ) / rpm } ; model / engine } ; 2.0 duratec he }
select the row whose time record of all rows is 2nd minimum . the athletes record of this row is mathieu goubel .
Output: eq { hop { nth_argmin { all_rows ; time ; 2 } ; athletes } ; mathieu goubel }
the average of the enrollment record of all rows is 3284 .
Output: round_eq { avg { all_rows ; enrollment } ; 3284 }
select the rows whose round record fuzzily matches to 3 . take the pick record of this row . select the rows whose round record fuzzily matches to 2 . take the pick record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; round ; 3 } ; pick } ; hop { filter_eq { all_rows ; round ; 2 } ; pick } }
select the rows whose outcome record fuzzily matches to winner . among these rows , select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; outcome ; winner } ; surface ; clay } } ; 4 }
select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table .
Output: only { filter_eq { all_rows ; surface ; carpet } }
for the acronym records of all rows , most of them fuzzily match to - .
Output: most_eq { all_rows ; acronym ; - }
select the rows whose manner of departure record fuzzily matches to sacked . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; sacked } } ; 4 }
the average of the first elected record of all rows is 1953.5 .
Output: round_eq { avg { all_rows ; first elected } ; 1953.5 }
the sum of the current record of all rows is 10 .
Output: round_eq { sum { all_rows ; current } ; 10 }
select the rows whose country record fuzzily matches to poland . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country ; poland } } ; 2 }
select the rows whose nation record fuzzily matches to canada ( can ) . take the silver record of this row . select the rows whose nation record fuzzily matches to puerto rico ( pur ) . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; canada ( can ) } ; silver } ; hop { filter_eq { all_rows ; nation ; puerto rico ( pur ) } ; silver } }
select the rows whose high rebounds record fuzzily matches to mcdyess . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; high rebounds ; mcdyess } } ; 2 }
select the rows whose tournament record fuzzily matches to the open championship . take the cuts made record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; cuts made } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made } }
select the row whose date record of all rows is minimum . the venue record of this row is mcg .
Output: eq { hop { argmin { all_rows ; date } ; venue } ; mcg }
select the rows whose cores record is equal to 1 . there is only one such row in the table . the processor record of this unqiue row is conroe - l .
Output: and { only { filter_eq { all_rows ; cores ; 1 } } ; eq { hop { filter_eq { all_rows ; cores ; 1 } ; processor } ; conroe - l } }
select the rows whose capacity record is greater than 10000 . the number of such rows is 5 .
Output: eq { count { filter_greater { all_rows ; capacity ; 10000 } } ; 5 }
select the rows whose frequency record fuzzily matches to 150 mhz . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; frequency ; 150 mhz } } ; 1 }
select the rows whose election record is equal to 2013 . for the inhabitants records of these rows , all of them are greater than or equal to 100000 .
Output: all_greater_eq { filter_eq { all_rows ; election ; 2013 } ; inhabitants ; 100000 }
select the rows whose launcher record fuzzily matches to saturn v . there is only one such row in the table . the flights record of this unqiue row is 10 lun / or .
Output: and { only { filter_eq { all_rows ; launcher ; saturn v } } ; eq { hop { filter_eq { all_rows ; launcher ; saturn v } ; flights } ; 10 lun / or } }
select the rows whose incumbent record fuzzily matches to george miller . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to cal dooley . 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 ; george miller } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; cal dooley } ; first elected } }
for the year records of all rows , most of them are equal to 1996 .
Output: most_eq { all_rows ; year ; 1996 }
select the row whose oilers points record of all rows is 3rd minimum . the date record of this row is oct 29 .
Output: eq { hop { nth_argmin { all_rows ; oilers points ; 3 } ; date } ; oct 29 }
select the rows whose network record fuzzily matches to the wb . the sum of the viewers in millions record of these rows is 7.62 .
Output: round_eq { sum { filter_eq { all_rows ; network ; the wb } ; viewers in millions } ; 7.62 }
select the row whose points record of all rows is maximum . the team record of this row is libertad .
Output: eq { hop { argmax { all_rows ; points } ; team } ; libertad }
the average of the crowd record of all rows is 24000 .
Output: round_eq { avg { all_rows ; crowd } ; 24000 }
select the rows whose date record fuzzily matches to august . among these rows , select the rows whose winning driver record fuzzily matches to maurício gugelmin . there is only one such row in the table . the winning team record of this unqiue row is pacwest .
Output: and { only { filter_eq { filter_eq { all_rows ; date ; august } ; winning driver ; maurício gugelmin } } ; eq { hop { filter_eq { filter_eq { all_rows ; date ; august } ; winning driver ; maurício gugelmin } ; winning team } ; pacwest } }
select the rows whose name record fuzzily matches to mario domínguez . take the qual 2 record of this row . select the rows whose name record fuzzily matches to alex sperafico . take the qual 2 record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; mario domínguez } ; qual 2 } ; hop { filter_eq { all_rows ; name ; alex sperafico } ; qual 2 } }
select the rows whose year record is equal to 1987 . the sum of the notes record of these rows is 4:27:46 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 1987 } ; notes } ; 4:27:46 }
select the rows whose gender record fuzzily matches to f . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; gender ; f } } ; 2 }
the average of the runs record of all rows is 5390 .
Output: round_eq { avg { all_rows ; runs } ; 5390 }
select the row whose time record of all rows is 2nd minimum . the country record of this row is czech republic .
Output: eq { hop { nth_argmin { all_rows ; time ; 2 } ; country } ; czech republic }
for the time records of all rows , most of them are less than 54.0 .
Output: most_less { all_rows ; time ; 54.0 }
select the rows whose shot pct record is greater than or equal to 80 % . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { all_rows ; shot pct ; 80 % } } ; 3 }
select the row whose events record of all rows is maximum . the player record of this row is jim thorpe .
Output: eq { hop { argmax { all_rows ; events } ; player } ; jim thorpe }
select the row whose years for grizzlies record of all rows is minimum . the player record of this row is eric leckner .
Output: eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; eric leckner }
select the rows whose location attendance record fuzzily matches to pepsi center . among these rows , select the rows whose location attendance record is greater than 19,500 . there is only one such row in the table .
Output: only { filter_greater { filter_eq { all_rows ; location attendance ; pepsi center } ; location attendance ; 19,500 } }
select the row whose events record of all rows is maximum . the tournament record of this row is pga championship .
Output: eq { hop { argmax { all_rows ; events } ; tournament } ; pga championship }
select the rows whose points ( total 500 ) record is greater than 300 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; points ( total 500 ) ; 300 } } ; 3 }
select the rows whose difference record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is santos .
Output: and { only { filter_eq { all_rows ; difference ; 6 } } ; eq { hop { filter_eq { all_rows ; difference ; 6 } ; team } ; santos } }
the average of the pick record of all rows is 154 .
Output: round_eq { avg { all_rows ; pick } ; 154 }
select the rows whose party record fuzzily matches to republican . there is only one such row in the table . the incumbent record of this unqiue row is jimmy quillen .
Output: and { only { filter_eq { all_rows ; party ; republican } } ; eq { hop { filter_eq { all_rows ; party ; republican } ; incumbent } ; jimmy quillen } }
select the rows whose team record fuzzily matches to yamaha . the average of the points record of these rows is 4.5 .
Output: round_eq { avg { filter_eq { all_rows ; team ; yamaha } ; points } ; 4.5 }
the 3rd minimum days held record of all rows is 196 .
Output: eq { nth_min { all_rows ; days held ; 3 } ; 196 }
select the rows whose ceased operation record fuzzily matches to failed at launch . there is only one such row in the table . the name record of this unqiue row is injun 2 .
Output: and { only { filter_eq { all_rows ; ceased operation ; failed at launch } } ; eq { hop { filter_eq { all_rows ; ceased operation ; failed at launch } ; name } ; injun 2 } }
the average of the crowd record of all rows is 17724 .
Output: round_eq { avg { all_rows ; crowd } ; 17724 }
the sum of the total record of all rows is 12 .
Output: round_eq { sum { all_rows ; total } ; 12 }
the maximum goals record of all rows is 15 . the scorer record of the row with superlative goals record is choi sang - kuk .
Output: and { eq { max { all_rows ; goals } ; 15 } ; eq { hop { argmax { all_rows ; goals } ; scorer } ; choi sang - kuk } }
select the rows whose competition record fuzzily matches to friendly match . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; competition ; friendly match } } ; 5 }
select the row whose attendance record of all rows is maximum . the week record of this row is 1 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 1 }
select the rows whose place record fuzzily matches to t2 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; place ; t2 } } ; 3 }
the average of the gold record of all rows is .67 .
Output: round_eq { avg { all_rows ; gold } ; .67 }
select the rows whose gender record fuzzily matches to f . among these rows , select the rows whose residence record fuzzily matches to halifax . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; gender ; f } ; residence ; halifax } } ; 2 }
select the rows whose bbc one presenter ( s ) record fuzzily matches to jake humphrey . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; bbc one presenter ( s ) ; jake humphrey } } ; 2 }
select the row whose agg record of all rows is maximum . the team 1 record of this row is manzini wanderers .
Output: eq { hop { argmax { all_rows ; agg } ; team 1 } ; manzini wanderers }
select the rows whose class record fuzzily matches to a . there is only one such row in the table . the city of license record of this unqiue row is kingston .
Output: and { only { filter_eq { all_rows ; class ; a } } ; eq { hop { filter_eq { all_rows ; class ; a } ; city of license } ; kingston } }
select the rows whose first aired record fuzzily matches to or . there is only one such row in the table . the episode title record of this unqiue row is the great american melting pot .
Output: and { only { filter_eq { all_rows ; first aired ; or } } ; eq { hop { filter_eq { all_rows ; first aired ; or } ; episode title } ; the great american melting pot } }
select the rows whose engine type record fuzzily matches to diesel - electric . there is only one such row in the table . the make & model record of this unqiue row is orion vii 3rd generation .
Output: and { only { filter_eq { all_rows ; engine type ; diesel - electric } } ; eq { hop { filter_eq { all_rows ; engine type ; diesel - electric } ; make & model } ; orion vii 3rd generation } }
select the row whose frequency record of all rows is 2nd maximum . the model number record of this row is turion 64 x2 tl - 66 .
Output: eq { hop { nth_argmax { all_rows ; frequency ; 2 } ; model number } ; turion 64 x2 tl - 66 }
select the rows whose position record is less than or equal to 5 . the average of the points record of these rows is 22 .
Output: round_eq { avg { filter_less_eq { all_rows ; position ; 5 } ; points } ; 22 }
select the row whose gross record of all rows is maximum . the title record of this row is et the extra - terrestrial .
Output: eq { hop { argmax { all_rows ; gross } ; title } ; et the extra - terrestrial }
select the row whose podiums record of all rows is maximum . the points record of this row is 357 .
Output: eq { hop { argmax { all_rows ; podiums } ; points } ; 357 }
select the row whose pick record of all rows is 2nd minimum . the player record of this row is burns mcpherson .
Output: eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; burns mcpherson }
select the rows whose season record fuzzily matches to 2005 . take the races record of this row . select the rows whose season record fuzzily matches to 2012 . take the races record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2005 } ; races } ; hop { filter_eq { all_rows ; season ; 2012 } ; races } }
select the rows whose number of electorates ( 2009 ) record is greater than 150000 . among these rows , select the rows whose number of electorates ( 2009 ) record is greater than 150000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; number of electorates ( 2009 ) ; 150000 } ; number of electorates ( 2009 ) ; 150000 } } ; 2 }
select the rows whose date successor seated record fuzzily matches to not filled this congress . there is only one such row in the table . the district record of this unqiue row is virginia 2nd .
Output: and { only { filter_eq { all_rows ; date successor seated ; not filled this congress } } ; eq { hop { filter_eq { all_rows ; date successor seated ; not filled this congress } ; district } ; virginia 2nd } }
select the row whose attempts record of all rows is 2nd maximum . the name record of this row is anthony thomas .
Output: eq { hop { nth_argmax { all_rows ; attempts ; 2 } ; name } ; anthony thomas }
select the rows whose launch date ( ddmmyyyy ) record fuzzily matches to 2005 . among these rows , select the rows whose connection speed record fuzzily matches to 236.8 kbit/s . there is only one such row in the table . the carrier record of this unqiue row is moldcell .
Output: and { only { filter_eq { filter_eq { all_rows ; launch date ( ddmmyyyy ) ; 2005 } ; connection speed ; 236.8 kbit/s } } ; eq { hop { filter_eq { filter_eq { all_rows ; launch date ( ddmmyyyy ) ; 2005 } ; connection speed ; 236.8 kbit/s } ; carrier } ; moldcell } }
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 .
Output: greater { hop { filter_eq { all_rows ; artist ; andreas lundstedt } ; points } ; hop { filter_eq { all_rows ; artist ; garmarna } ; points } }
select the rows whose player record fuzzily matches to ray lindwall . take the wickets record of this row . select the rows whose player record fuzzily matches to bill johnston . take the wickets record of this row . the first record is equal to the second record .
Output: eq { hop { filter_eq { all_rows ; player ; ray lindwall } ; wickets } ; hop { filter_eq { all_rows ; player ; bill johnston } ; wickets } }
the sum of the crowd record of all rows is 80962 .
Output: round_eq { sum { all_rows ; crowd } ; 80962 }
for the high assists records of all rows , most of them fuzzily match to whalen .
Output: most_eq { all_rows ; high assists ; whalen }
select the row whose won record of all rows is 2nd maximum . the club record of this row is treorchy rfc .
Output: eq { hop { nth_argmax { all_rows ; won ; 2 } ; club } ; treorchy rfc }
select the rows whose player record fuzzily matches to jerry grote . take the from record of this row . select the rows whose player record fuzzily matches to andrew goudelock . take the from record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; jerry grote } ; from } ; hop { filter_eq { all_rows ; player ; andrew goudelock } ; from } }
select the rows whose country record fuzzily matches to portugal . take the rank record of this row . select the rows whose country record fuzzily matches to cuba . take the rank record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; country ; portugal } ; rank } ; hop { filter_eq { all_rows ; country ; cuba } ; rank } }
the average of the score record of all rows is 1.67 .
Output: round_eq { avg { all_rows ; score } ; 1.67 }
select the rows whose incumbent record fuzzily matches to jack thomas brinkley . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to dawson mathis . 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 ; jack thomas brinkley } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; dawson mathis } ; first elected } }
select the rows whose method record fuzzily matches to no contest . there is only one such row in the table . the opponent record of this unqiue row is rob emerson .
Output: and { only { filter_eq { all_rows ; method ; no contest } } ; eq { hop { filter_eq { all_rows ; method ; no contest } ; opponent } ; rob emerson } }
select the rows whose region record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; region } } ; 5 }
select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st joseph 's catholic school .
Output: and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st joseph 's catholic school } }
select the rows whose length record is less than 1:00 . there is only one such row in the table . the title record of this unqiue row is intro .
Output: and { only { filter_less { all_rows ; length ; 1:00 } } ; eq { hop { filter_less { all_rows ; length ; 1:00 } ; title } ; intro } }
the average of the total record of all rows is 145.5 .
Output: round_eq { avg { all_rows ; total } ; 145.5 }
select the rows whose winning driver record fuzzily matches to jean behra . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; winning driver ; jean behra } } ; 4 }
select the rows whose location record fuzzily matches to kohl center madison , wi . there is only one such row in the table . the acc team record of this unqiue row is nc state .
Output: and { only { filter_eq { all_rows ; location ; kohl center madison , wi } } ; eq { hop { filter_eq { all_rows ; location ; kohl center madison , wi } ; acc team } ; nc state } }
select the row whose time record of all rows is minimum . the nationality record of this row is australia .
Output: eq { hop { argmin { all_rows ; time } ; nationality } ; australia }
select the rows whose decile record is equal to 9 . there is only one such row in the table . the name record of this unqiue row is elsthorpe school .
Output: and { only { filter_eq { all_rows ; decile ; 9 } } ; eq { hop { filter_eq { all_rows ; decile ; 9 } ; name } ; elsthorpe school } }
select the rows whose name record fuzzily matches to ryan lamb . take the tries record of this row . select the rows whose name record fuzzily matches to shane drahm . take the tries record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; ryan lamb } ; tries } ; hop { filter_eq { all_rows ; name ; shane drahm } ; tries } }
select the row whose apps record of all rows is maximum . the name record of this row is john hall .
Output: eq { hop { argmax { all_rows ; apps } ; name } ; john hall }
select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row . select the rows whose country record fuzzily matches to uzbekistan . take the penalty record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; country ; nigeria } ; penalty } ; hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty } }