niryuu/sni-each-converted huggingface dataset:
Command: all_eq { all_rows ; date ; 17 june 1939 }, interpretation: for the date records of all rows , all of them fuzzily match to 17 june 1939 .
Output: yes
Command: all_eq { all_rows ; date ; 17 june 1939 }, interpretation: select the rows whose region record fuzzily matches to united states or japan . for the format records of these rows , most of them fuzzily match to cd .
Output: no
Command: most_eq { filter_eq { all_rows ; region ; united states or japan } ; format ; cd }, interpretation: select the rows whose region record fuzzily matches to united states or japan . for the format records of these rows , most of them fuzzily match to cd .
Output: yes
Command: most_eq { filter_eq { all_rows ; region ; united states or japan } ; format ; cd }, interpretation: select the rows whose elevation record is greater than 12000 feet . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; elevation ; 12000 feet } } ; 3 }, interpretation: select the rows whose elevation record is greater than 12000 feet . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; elevation ; 12000 feet } } ; 3 }, interpretation: select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d . there is only one such row in the table . the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .
Output: no
Command: and { only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } } ; and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } } }, interpretation: select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d . there is only one such row in the table . the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .
Output: yes
Command: and { only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } } ; and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } } }, interpretation: select the row whose height ( cm ) record of all rows is 2nd maximum . the name record of this row is al iafrate .
Output: no
Command: eq { hop { nth_argmax { all_rows ; height ( cm ) ; 2 } ; name } ; al iafrate }, interpretation: select the row whose height ( cm ) record of all rows is 2nd maximum . the name record of this row is al iafrate .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; height ( cm ) ; 2 } ; name } ; al iafrate }, interpretation: select the rows whose finish record is equal to 14 . there is only one such row in the table . the year record of this unqiue row is 1982 .
Output: no
Command: and { only { filter_eq { all_rows ; finish ; 14 } } ; eq { hop { filter_eq { all_rows ; finish ; 14 } ; year } ; 1982 } }, interpretation: select the rows whose finish record is equal to 14 . there is only one such row in the table . the year record of this unqiue row is 1982 .
Output: yes
Command: and { only { filter_eq { all_rows ; finish ; 14 } } ; eq { hop { filter_eq { all_rows ; finish ; 14 } ; year } ; 1982 } }, interpretation: select the rows whose frequency record fuzzily matches to 830 khz . there is only one such row in the table . the calls record of this unqiue row is wcrn .
Output: no
Command: and { only { filter_eq { all_rows ; frequency ; 830 khz } } ; eq { hop { filter_eq { all_rows ; frequency ; 830 khz } ; calls } ; wcrn } }, interpretation: select the rows whose frequency record fuzzily matches to 830 khz . there is only one such row in the table . the calls record of this unqiue row is wcrn .
Output: yes
Command: and { only { filter_eq { all_rows ; frequency ; 830 khz } } ; eq { hop { filter_eq { all_rows ; frequency ; 830 khz } ; calls } ; wcrn } }, interpretation: select the rows whose callsign record fuzzily matches to dwll . take the power record of this row . select the rows whose callsign record fuzzily matches to dyku . take the power record of this row . the first record is 10 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; callsign ; dwll } ; power } ; hop { filter_eq { all_rows ; callsign ; dyku } ; power } } ; 10 }, interpretation: select the rows whose callsign record fuzzily matches to dwll . take the power record of this row . select the rows whose callsign record fuzzily matches to dyku . take the power record of this row . the first record is 10 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; callsign ; dwll } ; power } ; hop { filter_eq { all_rows ; callsign ; dyku } ; power } } ; 10 }, interpretation: select the rows whose year record fuzzily matches to sophomore . the average of the weight record of these rows is 192.5 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; year ; sophomore } ; weight } ; 192.5 }, interpretation: select the rows whose year record fuzzily matches to sophomore . the average of the weight record of these rows is 192.5 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; year ; sophomore } ; weight } ; 192.5 }, interpretation: select the rows whose nationality record fuzzily matches to united states . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; nationality ; united states } } ; 3 }, interpretation: select the rows whose nationality record fuzzily matches to united states . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; nationality ; united states } } ; 3 }, interpretation: for the nationality records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; nationality ; united states }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is william j driver .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; william j driver }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is william j driver .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; william j driver }, interpretation: for the class records of all rows , all of them fuzzily match to d .
Output: no
Command: all_eq { all_rows ; class ; d }, interpretation: select the row whose points record of all rows is 3rd maximum . the rider record of this row is mick grant .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 3 } ; rider } ; mick grant }, interpretation: select the row whose points record of all rows is 3rd maximum . the rider record of this row is mick grant .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 3 } ; rider } ; mick grant }, interpretation: select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 3rd minimum . the attendance record of this row is 51925 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 3 } ; attendance } ; 51925 }, interpretation: select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 3rd minimum . the attendance record of this row is 51925 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 3 } ; attendance } ; 51925 }, interpretation: the average of the finish position record of all rows is 29.8 .
Output: no
Command: round_eq { avg { all_rows ; finish position } ; 29.8 }, interpretation: the average of the finish position record of all rows is 29.8 .
Output: yes
Command: round_eq { avg { all_rows ; finish position } ; 29.8 }, interpretation: select the rows whose player record fuzzily matches to julien cayer . take the overall pick record of this row . select the rows whose player record fuzzily matches to gustav nyquist . take the overall pick record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; julien cayer } ; overall pick } ; hop { filter_eq { all_rows ; player ; gustav nyquist } ; overall pick } }, interpretation: select the rows whose player record fuzzily matches to julien cayer . take the overall pick record of this row . select the rows whose player record fuzzily matches to gustav nyquist . take the overall pick record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; julien cayer } ; overall pick } ; hop { filter_eq { all_rows ; player ; gustav nyquist } ; overall pick } }, interpretation: select the rows whose date record fuzzily matches to february . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; february } } ; 4 }, interpretation: select the rows whose date record fuzzily matches to february . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; february } } ; 4 }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; surface ; hard }, interpretation: select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row . select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row . the second record is 2 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } } ; -2 }, interpretation: select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row . select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row . the second record is 2 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } } ; -2 }, interpretation: select the rows whose the mole record is arbitrary . the number of such rows is 14 .
Output: no
Command: eq { count { filter_all { all_rows ; the mole } } ; 14 }, interpretation: select the rows whose the mole record is arbitrary . the number of such rows is 14 .
Output: yes
Command: eq { count { filter_all { all_rows ; the mole } } ; 14 }, interpretation: select the rows whose tournament record fuzzily matches to taipei , taiwan . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; taipei , taiwan } } ; 2 }, interpretation: select the rows whose tournament record fuzzily matches to taipei , taiwan . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; taipei , taiwan } } ; 2 }, interpretation: select the row whose high points record of all rows is maximum . the high points record of this row is dalembert ( 23 ) .
Output: no
Command: eq { hop { argmax { all_rows ; high points } ; high points } ; dalembert ( 23 ) }, interpretation: select the row whose high points record of all rows is maximum . the high points record of this row is dalembert ( 23 ) .
Output: yes
Command: eq { hop { argmax { all_rows ; high points } ; high points } ; dalembert ( 23 ) }, interpretation: select the rows whose currency record fuzzily matches to lithuanian litas . take the entry erm ii record of this row . select the rows whose currency record fuzzily matches to latvian lats . take the entry erm ii record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; currency ; lithuanian litas } ; entry erm ii } ; hop { filter_eq { all_rows ; currency ; latvian lats } ; entry erm ii } }, interpretation: select the rows whose currency record fuzzily matches to lithuanian litas . take the entry erm ii record of this row . select the rows whose currency record fuzzily matches to latvian lats . take the entry erm ii record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; currency ; lithuanian litas } ; entry erm ii } ; hop { filter_eq { all_rows ; currency ; latvian lats } ; entry erm ii } }, interpretation: select the rows whose department record is arbitrary . the number of such rows is 7 .
Output: no
Command: eq { count { filter_all { all_rows ; department } } ; 7 }, interpretation: select the rows whose department record is arbitrary . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_all { all_rows ; department } } ; 7 }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is harry lane englebright .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; harry lane englebright }, interpretation: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is harry lane englebright .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; harry lane englebright }, interpretation: for the time slot ( est ) records of all rows , most of them fuzzily match to wednesday 10 pm / 9c .
Output: no
Command: most_eq { all_rows ; time slot ( est ) ; wednesday 10 pm / 9c }, interpretation: for the time slot ( est ) records of all rows , most of them fuzzily match to wednesday 10 pm / 9c .
Output: yes
Command: most_eq { all_rows ; time slot ( est ) ; wednesday 10 pm / 9c }, interpretation: select the row whose laps record of all rows is maximum . the year record of this row is 2007 .
Output: no
Command: eq { hop { argmax { all_rows ; laps } ; year } ; 2007 }, interpretation: select the row whose laps record of all rows is maximum . the year record of this row is 2007 .
Output: yes
Command: eq { hop { argmax { all_rows ; laps } ; year } ; 2007 }, interpretation: select the row whose tied record of all rows is maximum . the season record of this row is 1968 - 69 .
Output: no
Command: eq { hop { argmax { all_rows ; tied } ; season } ; 1968 - 69 }, interpretation: select the row whose tied record of all rows is maximum . the season record of this row is 1968 - 69 .
Output: yes
Command: eq { hop { argmax { all_rows ; tied } ; season } ; 1968 - 69 }, interpretation: select the row whose money record of all rows is 4th maximum . the score record of this row is 68 + 68 + 69 + 79 = 284 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; money ; 4 } ; score } ; 68 + 68 + 69 + 79, interpretation: select the row whose money record of all rows is 4th maximum . the score record of this row is 68 + 68 + 69 + 79 = 284 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; money ; 4 } ; score } ; 68 + 68 + 69 + 79, interpretation: select the rows whose college record fuzzily matches to north carolina . there is only one such row in the table . the name record of this unqiue row is micah moon .
Output: no
Command: and { only { filter_eq { all_rows ; college ; north carolina } } ; eq { hop { filter_eq { all_rows ; college ; north carolina } ; name } ; micah moon } }, interpretation: select the rows whose college record fuzzily matches to north carolina . there is only one such row in the table . the name record of this unqiue row is micah moon .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; north carolina } } ; eq { hop { filter_eq { all_rows ; college ; north carolina } ; name } ; micah moon } }, interpretation: select the rows whose top - 25 record is equal to 6 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .
Output: no
Command: and { only { filter_eq { all_rows ; top - 25 ; 6 } } ; eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament } }, interpretation: select the rows whose top - 25 record is equal to 6 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .
Output: yes
Command: and { only { filter_eq { all_rows ; top - 25 ; 6 } } ; eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament } }, interpretation: select the rows whose team record fuzzily matches to palmeiras . take the against record of this row . select the rows whose team record fuzzily matches to flamengo . take the against record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; team ; palmeiras } ; against } ; hop { filter_eq { all_rows ; team ; flamengo } ; against } }, interpretation: select the rows whose team record fuzzily matches to palmeiras . take the against record of this row . select the rows whose team record fuzzily matches to flamengo . take the against record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; team ; palmeiras } ; against } ; hop { filter_eq { all_rows ; team ; flamengo } ; against } }, interpretation: for the venue records of all rows , most of them fuzzily match to lansdowne road , dublin , ireland .
Output: no
Command: most_eq { all_rows ; venue ; lansdowne road , dublin , ireland }, interpretation: for the venue records of all rows , most of them fuzzily match to lansdowne road , dublin , ireland .
Output: yes
Command: most_eq { all_rows ; venue ; lansdowne road , dublin , ireland }, interpretation: the sum of the gold record of all rows is 36 .
Output: no
Command: round_eq { sum { all_rows ; gold } ; 36 }, interpretation: the sum of the gold record of all rows is 36 .
Output: yes
Command: round_eq { sum { all_rows ; gold } ; 36 }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is frank wolf .
Output: no
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; frank wolf }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is frank wolf .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; frank wolf }, interpretation: for the score records of all rows , most of them are greater than or equal to 70 .
Output: no
Command: most_greater_eq { all_rows ; score ; 70 }, interpretation: for the score records of all rows , most of them are greater than or equal to 70 .
Output: yes
Command: most_greater_eq { all_rows ; score ; 70 }, interpretation: the sum of the attendance record of all rows is 476,381 .
Output: no
Command: round_eq { sum { all_rows ; attendance } ; 476,381 }, interpretation: the sum of the attendance record of all rows is 476,381 .
Output: yes
Command: round_eq { sum { all_rows ; attendance } ; 476,381 }, interpretation: for the headquarters records of all rows , most of them fuzzily match to sweden .
Output: no
Command: most_eq { all_rows ; headquarters ; sweden }, interpretation: for the headquarters records of all rows , most of them fuzzily match to sweden .
Output: yes
Command: most_eq { all_rows ; headquarters ; sweden }, interpretation: for the round records of all rows , all of them are equal to 1 .
Output: no
Command: all_eq { all_rows ; round ; 1 }, interpretation: for the round records of all rows , all of them are equal to 1 .
Output: yes
Command: all_eq { all_rows ; round ; 1 }, interpretation: select the rows whose sp record fuzzily matches to 10 / 1 . select the row whose position record of these rows is 1st minimum . the jockey record of this row is richard johnson .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; sp ; 10 / 1 } ; position ; 1 } ; jockey } ; richard johnson }, interpretation: select the rows whose sp record fuzzily matches to 10 / 1 . select the row whose position record of these rows is 1st minimum . the jockey record of this row is richard johnson .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; sp ; 10 / 1 } ; position ; 1 } ; jockey } ; richard johnson }, interpretation: for the monounsaturated fat records of all rows , most of them are greater than or equal to 20 g .
Output: no
Command: most_greater_eq { all_rows ; monounsaturated fat ; 20 g }, interpretation: for the monounsaturated fat records of all rows , most of them are greater than or equal to 20 g .
Output: yes
Command: most_greater_eq { all_rows ; monounsaturated fat ; 20 g }, interpretation: select the rows whose gold record is equal to 1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; gold ; 1 } } ; 2 }, interpretation: select the rows whose gold record is equal to 1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; gold ; 1 } } ; 2 }, interpretation: for the surface records of all rows , most of them fuzzily match to clay .
Output: no
Command: most_eq { all_rows ; surface ; clay }, interpretation: the sum of the rounds record of all rows is 122 .
Output: no
Command: round_eq { sum { all_rows ; rounds } ; 122 }, interpretation: the sum of the rounds record of all rows is 122 .
Output: yes
Command: round_eq { sum { all_rows ; rounds } ; 122 }, interpretation: for the award ceremony records of all rows , most of them fuzzily match to drama desk award .
Output: no
Command: most_eq { all_rows ; award ceremony ; drama desk award }, interpretation: select the rows whose pos record fuzzily matches to dnf . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; pos ; dnf } } ; 7 }, interpretation: select the rows whose pos record fuzzily matches to dnf . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; pos ; dnf } } ; 7 }, interpretation: select the row whose no of installments record of all rows is 2nd maximum . the book series record of this row is the hardy boys .
Output: no
Command: eq { hop { nth_argmax { all_rows ; no of installments ; 2 } ; book series } ; the hardy boys }, interpretation: select the row whose no of installments record of all rows is 2nd maximum . the book series record of this row is the hardy boys .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; no of installments ; 2 } ; book series } ; the hardy boys }, interpretation: for the crowd records of all rows , most of them are greater than 10000 .
Output: no
Command: most_greater { all_rows ; crowd ; 10000 }, interpretation: select the rows whose school record fuzzily matches to hawaii . there is only one such row in the table . the player record of this unqiue row is darrick branch .
Output: no
Command: and { only { filter_eq { all_rows ; school ; hawaii } } ; eq { hop { filter_eq { all_rows ; school ; hawaii } ; player } ; darrick branch } }, interpretation: select the rows whose school record fuzzily matches to hawaii . there is only one such row in the table . the player record of this unqiue row is darrick branch .
Output: yes
Command: and { only { filter_eq { all_rows ; school ; hawaii } } ; eq { hop { filter_eq { all_rows ; school ; hawaii } ; player } ; darrick branch } }, interpretation: select the rows whose high rebounds record fuzzily matches to chris bosh . the number of such rows is 9 .
Output: no
Command: eq { count { filter_eq { all_rows ; high rebounds ; chris bosh } } ; 9 }, interpretation: select the rows whose high rebounds record fuzzily matches to chris bosh . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_eq { all_rows ; high rebounds ; chris bosh } } ; 9 }, interpretation: select the rows whose opponent record fuzzily matches to chicago bears . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 47475 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance } ; 47475 }, interpretation: select the rows whose opponent record fuzzily matches to chicago bears . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 47475 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance } ; 47475 }, interpretation: the average of the goals record of all rows is 26.2 .
Output: no
Command: round_eq { avg { all_rows ; goals } ; 26.2 }, interpretation: the average of the goals record of all rows is 26.2 .
Output: yes
Command: round_eq { avg { all_rows ; goals } ; 26.2 }, interpretation: the average of the episode record of all rows is 32.5 .
Output: no
Command: round_eq { avg { all_rows ; episode } ; 32.5 }, interpretation: the average of the episode record of all rows is 32.5 .
Output: yes
Command: round_eq { avg { all_rows ; episode } ; 32.5 }, interpretation: select the rows whose team record fuzzily matches to east germany . take the lost record of this row . select the rows whose team record fuzzily matches to poland . take the lost record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; team ; east germany } ; lost } ; hop { filter_eq { all_rows ; team ; poland } ; lost } }, interpretation: select the rows whose team record fuzzily matches to east germany . take the lost record of this row . select the rows whose team record fuzzily matches to poland . take the lost record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; team ; east germany } ; lost } ; hop { filter_eq { all_rows ; team ; poland } ; lost } }, interpretation: select the rows whose manufacturer record fuzzily matches to suzuki . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; manufacturer ; suzuki } } ; 2 }, interpretation: select the rows whose manufacturer record fuzzily matches to suzuki . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; manufacturer ; suzuki } } ; 2 }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is united states ( usa ) .
Output: no
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; united states ( usa ) }, interpretation: select the rows whose team ( league ) record fuzzily matches to london knights . there is only one such row in the table . the player record of this unqiue row is jim sandlak .
Output: no
Command: and { only { filter_eq { all_rows ; team ( league ) ; london knights } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; london knights } ; player } ; jim sandlak } }, interpretation: select the rows whose team ( league ) record fuzzily matches to london knights . there is only one such row in the table . the player record of this unqiue row is jim sandlak .
Output: yes
Command: and { only { filter_eq { all_rows ; team ( league ) ; london knights } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; london knights } ; player } ; jim sandlak } }, interpretation: for the position records of all rows , most of them fuzzily match to defence .
Output: no
Command: most_eq { all_rows ; position ; defence }, interpretation: for the position records of all rows , most of them fuzzily match to defence .
Output: yes
Command: most_eq { all_rows ; position ; defence }, interpretation: select the rows whose tournament record fuzzily matches to jordan fa shield . take the draws record of this row . select the rows whose tournament record fuzzily matches to jordan super cup . take the draws record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; tournament ; jordan fa shield } ; draws } ; hop { filter_eq { all_rows ; tournament ; jordan super cup } ; draws } }, interpretation: select the rows whose tournament record fuzzily matches to jordan fa shield . take the draws record of this row . select the rows whose tournament record fuzzily matches to jordan super cup . take the draws record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; tournament ; jordan fa shield } ; draws } ; hop { filter_eq { all_rows ; tournament ; jordan super cup } ; draws } }, interpretation: select the rows whose cfl team record fuzzily matches to hamilton tiger - cats . take the pick record of this row . select the rows whose cfl team record fuzzily matches to british columbia lions . take the pick record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; cfl team ; hamilton tiger - cats } ; pick } ; hop { filter_eq { all_rows ; cfl team ; british columbia lions } ; pick } }, interpretation: select the rows whose cfl team record fuzzily matches to hamilton tiger - cats . take the pick record of this row . select the rows whose cfl team record fuzzily matches to british columbia lions . take the pick record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; cfl team ; hamilton tiger - cats } ; pick } ; hop { filter_eq { all_rows ; cfl team ; british columbia lions } ; pick } }, interpretation: select the rows whose position record fuzzily matches to running back . among these rows , select the rows whose round record is greater than 4 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; position ; running back } ; round ; 4 } } ; 3 }, interpretation: select the rows whose position record fuzzily matches to running back . among these rows , select the rows whose round record is greater than 4 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; position ; running back } ; round ; 4 } } ; 3 }, interpretation: select the rows whose chassis record fuzzily matches to lola . there is only one such row in the table . the year record of this unqiue row is 1993 .
Output: no
Command: and { only { filter_eq { all_rows ; chassis ; lola } } ; eq { hop { filter_eq { all_rows ; chassis ; lola } ; year } ; 1993 } }, interpretation: select the rows whose chassis record fuzzily matches to lola . there is only one such row in the table . the year record of this unqiue row is 1993 .
Output: yes
Command: and { only { filter_eq { all_rows ; chassis ; lola } } ; eq { hop { filter_eq { all_rows ; chassis ; lola } ; year } ; 1993 } }, interpretation: the average of the average ratings record of all rows is 11.76 % .
Output: no
Command: round_eq { avg { all_rows ; average ratings } ; 11.76 % }, interpretation: the average of the average ratings record of all rows is 11.76 % .
Output: yes
Command: round_eq { avg { all_rows ; average ratings } ; 11.76 % }, interpretation: select the row whose years for jazz record of all rows is 2nd minimum . the player record of this row is j j anderson .
Output: no
Command: eq { hop { nth_argmin { all_rows ; years for jazz ; 2 } ; player } ; j j anderson }, interpretation: select the row whose years for jazz record of all rows is 2nd minimum . the player record of this row is j j anderson .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; years for jazz ; 2 } ; player } ; j j anderson }, interpretation: select the rows whose nation record fuzzily matches to ecuador . take the 2011 ( imf ) record of this row . select the rows whose nation record fuzzily matches to paraguay . take the 2011 ( imf ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nation ; ecuador } ; 2011 ( imf ) } ; hop { filter_eq { all_rows ; nation ; paraguay } ; 2011 ( imf ) } }, interpretation: select the rows whose nation record fuzzily matches to ecuador . take the 2011 ( imf ) record of this row . select the rows whose nation record fuzzily matches to paraguay . take the 2011 ( imf ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nation ; ecuador } ; 2011 ( imf ) } ; hop { filter_eq { all_rows ; nation ; paraguay } ; 2011 ( imf ) } }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; surface ; hard }, interpretation: the average of the high points record of all rows is 27-28 .
Output: no
Command: round_eq { avg { all_rows ; high points } ; 27-28 }, interpretation: the average of the high points record of all rows is 27-28 .
Output: yes
Command: round_eq { avg { all_rows ; high points } ; 27-28 }, interpretation: select the row whose silver record of all rows is 1st maximum . the nation record of this row is france .
Output: no
Command: eq { hop { nth_argmax { all_rows ; silver ; 1 } ; nation } ; france }, interpretation: select the row whose silver record of all rows is 1st maximum . the nation record of this row is france .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; silver ; 1 } ; nation } ; france }, interpretation: select the row whose negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of all rows is 2nd maximum . the currency record of this row is us dollar .
Output: no
Command: eq { hop { nth_argmax { all_rows ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) ; 2 } ; currency } ; us dollar }, interpretation: select the row whose negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of all rows is 2nd maximum . the currency record of this row is us dollar .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) ; 2 } ; currency } ; us dollar }, interpretation: select the rows whose score record fuzzily matches to 1 - 0 . among these rows , select the rows whose attendance record fuzzily matches to february . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; 1 - 0 } ; attendance ; february } } ; 2 }, interpretation: select the rows whose score record fuzzily matches to 1 - 0 . among these rows , select the rows whose attendance record fuzzily matches to february . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; 1 - 0 } ; attendance ; february } } ; 2 }, interpretation: select the rows whose lost record is equal to 17 . there is only one such row in the table . the club record of this unqiue row is milford haven rfc .
Output: no
Command: and { only { filter_eq { all_rows ; lost ; 17 } } ; eq { hop { filter_eq { all_rows ; lost ; 17 } ; club } ; milford haven rfc } }, interpretation: select the rows whose lost record is equal to 17 . there is only one such row in the table . the club record of this unqiue row is milford haven rfc .
Output: yes
Command: and { only { filter_eq { all_rows ; lost ; 17 } } ; eq { hop { filter_eq { all_rows ; lost ; 17 } ; club } ; milford haven rfc } }, interpretation: select the rows whose name record fuzzily matches to adam gilchrist ( wk ) . take the innings record of this row . select the rows whose name record fuzzily matches to matthew hayden . take the innings record of this row . the first record is greater than the second record . the innings record of the first row is 8 . the innings record of the second row is 6 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; name ; adam gilchrist ( wk ) } ; innings } ; hop { filter_eq { all_rows ; name ; matthew hayden } ; innings } } ; and { eq { hop { filter_eq { all_rows ; name ; adam gilchrist ( wk ) } ; innings } ; 8 } ; eq { hop { filter_eq { all_rows ; name ; matthew hayden } ; innings } ; 6 } } }, interpretation: select the rows whose name record fuzzily matches to adam gilchrist ( wk ) . take the innings record of this row . select the rows whose name record fuzzily matches to matthew hayden . take the innings record of this row . the first record is greater than the second record . the innings record of the first row is 8 . the innings record of the second row is 6 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; name ; adam gilchrist ( wk ) } ; innings } ; hop { filter_eq { all_rows ; name ; matthew hayden } ; innings } } ; and { eq { hop { filter_eq { all_rows ; name ; adam gilchrist ( wk ) } ; innings } ; 8 } ; eq { hop { filter_eq { all_rows ; name ; matthew hayden } ; innings } ; 6 } } }, interpretation: select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young } } ; 2 }, interpretation: select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to february 2 . take the attendance record of this row . select the rows whose date record fuzzily matches to february 29 . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; february 2 } ; attendance } ; hop { filter_eq { all_rows ; date ; february 29 } ; attendance } }, interpretation: select the rows whose date record fuzzily matches to february 2 . take the attendance record of this row . select the rows whose date record fuzzily matches to february 29 . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; february 2 } ; attendance } ; hop { filter_eq { all_rows ; date ; february 29 } ; attendance } }, interpretation: select the row whose position in 1992 record of all rows is minimum . the team record of this row is dinamo minsk .
Output: no
Command: eq { hop { argmin { all_rows ; position in 1992 } ; team } ; dinamo minsk }, interpretation: select the row whose position in 1992 record of all rows is minimum . the team record of this row is dinamo minsk .
Output: yes
Command: eq { hop { argmin { all_rows ; position in 1992 } ; team } ; dinamo minsk }, interpretation: select the rows whose class record fuzzily matches to 6ai . take the quantity record of this row . select the rows whose class record fuzzily matches to 6d . take the quantity record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; class ; 6ai } ; quantity } ; hop { filter_eq { all_rows ; class ; 6d } ; quantity } }, interpretation: select the rows whose class record fuzzily matches to 6ai . take the quantity record of this row . select the rows whose class record fuzzily matches to 6d . take the quantity record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; class ; 6ai } ; quantity } ; hop { filter_eq { all_rows ; class ; 6d } ; quantity } }, interpretation: select the rows whose release date record fuzzily matches to 2004 . there is only one such row in the table . the version record of this unqiue row is 4.10 .
Output: no
Command: and { only { filter_eq { all_rows ; release date ; 2004 } } ; eq { hop { filter_eq { all_rows ; release date ; 2004 } ; version } ; 4.10 } }, interpretation: select the rows whose release date record fuzzily matches to 2004 . there is only one such row in the table . the version record of this unqiue row is 4.10 .
Output: yes
Command: and { only { filter_eq { all_rows ; release date ; 2004 } } ; eq { hop { filter_eq { all_rows ; release date ; 2004 } ; version } ; 4.10 } }, interpretation: select the rows whose constellation record fuzzily matches to ursa major . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; constellation ; ursa major } } ; 3 }, interpretation: select the rows whose constellation record fuzzily matches to ursa major . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; constellation ; ursa major } } ; 3 }, interpretation: the average of the roll record of all rows is 338.66 .
Output: no
Command: round_eq { avg { all_rows ; roll } ; 338.66 }, interpretation: the average of the roll record of all rows is 338.66 .
Output: yes
Command: round_eq { avg { all_rows ; roll } ; 338.66 }, interpretation: select the row whose registered voters record of all rows is 3rd maximum . the city record of this row is encinitas .
Output: no
Command: eq { hop { nth_argmax { all_rows ; registered voters ; 3 } ; city } ; encinitas }, interpretation: select the row whose registered voters record of all rows is 3rd maximum . the city record of this row is encinitas .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; registered voters ; 3 } ; city } ; encinitas }, interpretation: for the time records of all rows , most of them are less than 4:00 .
Output: no
Command: most_less { all_rows ; time ; 4:00 }, interpretation: for the time records of all rows , most of them are less than 4:00 .
Output: yes
Command: most_less { all_rows ; time ; 4:00 }, interpretation: select the rows whose tournament record fuzzily matches to us open . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; tournament ; us open } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } }, interpretation: select the rows whose tournament record fuzzily matches to us open . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; tournament ; us open } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } }, interpretation: select the rows whose to par record fuzzily matches to - 9 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; to par ; - 9 } } ; 4 }, interpretation: select the rows whose to par record fuzzily matches to - 9 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; to par ; - 9 } } ; 4 }, interpretation: for the high points records of all rows , most of them fuzzily match to jason richardson .
Output: no
Command: most_eq { all_rows ; high points ; jason richardson }, interpretation: for the high points records of all rows , most of them fuzzily match to jason richardson .
Output: yes
Command: most_eq { all_rows ; high points ; jason richardson }, interpretation: for the founded records of all rows , most of them fuzzily match to 18 .
Output: no
Command: most_eq { all_rows ; founded ; 18 }, interpretation: for the founded records of all rows , most of them fuzzily match to 18 .
Output: yes
Command: most_eq { all_rows ; founded ; 18 }, interpretation: the average of the home team score record of all rows is 15.18 .
Output: no
Command: round_eq { avg { all_rows ; home team score } ; 15.18 }, interpretation: the average of the home team score record of all rows is 15.18 .
Output: yes
Command: round_eq { avg { all_rows ; home team score } ; 15.18 }, interpretation: select the rows whose nbr class record fuzzily matches to 251 . take the total record of this row . select the rows whose nbr class record fuzzily matches to 229 . take the total record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nbr class ; 251 } ; total } ; hop { filter_eq { all_rows ; nbr class ; 229 } ; total } }, interpretation: select the rows whose nbr class record fuzzily matches to 251 . take the total record of this row . select the rows whose nbr class record fuzzily matches to 229 . take the total record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nbr class ; 251 } ; total } ; hop { filter_eq { all_rows ; nbr class ; 229 } ; total } }, interpretation: select the rows whose points record is greater than or equal to 55 . among these rows , select the rows whose opponent record fuzzily matches to new york rangers . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater_eq { all_rows ; points ; 55 } ; opponent ; new york rangers } } ; 2 }, interpretation: select the rows whose points record is greater than or equal to 55 . among these rows , select the rows whose opponent record fuzzily matches to new york rangers . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater_eq { all_rows ; points ; 55 } ; opponent ; new york rangers } } ; 2 }, interpretation: for the founded records of all rows , most of them are less than 1900 .
Output: no
Command: most_less { all_rows ; founded ; 1900 }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 3.4 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 3.4 }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 3.4 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 3.4 }, interpretation: select the rows whose week record fuzzily matches to 1 . take the attendance record of this row . select the rows whose week record fuzzily matches to 15 . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; week ; 1 } ; attendance } ; hop { filter_eq { all_rows ; week ; 15 } ; attendance } }, interpretation: select the rows whose week record fuzzily matches to 1 . take the attendance record of this row . select the rows whose week record fuzzily matches to 15 . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; week ; 1 } ; attendance } ; hop { filter_eq { all_rows ; week ; 15 } ; attendance } }, interpretation: select the rows whose format record fuzzily matches to country . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; format ; country } } ; 2 }, interpretation: select the rows whose format record fuzzily matches to country . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; format ; country } } ; 2 }, interpretation: select the row whose crowd record of all rows is maximum . the date record of this row is friday , 1 august .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; date } ; friday , 1 august }, interpretation: select the row whose crowd record of all rows is maximum . the date record of this row is friday , 1 august .
Output: yes
Command: eq { hop { argmax { all_rows ; crowd } ; date } ; friday , 1 august }, interpretation: select the rows whose location attendance record fuzzily matches to at & t center . the average of the location attendance record of these rows is 18581 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance } ; 18581 }, interpretation: select the rows whose location attendance record fuzzily matches to at & t center . the average of the location attendance record of these rows is 18581 .
Output: yes