diff --git a/leaderboard_submissions/submission_zai-org-GLM-5.2.json b/leaderboard_submissions/submission_zai-org-GLM-5.2.json new file mode 100644 index 000000000..3cb686965 --- /dev/null +++ b/leaderboard_submissions/submission_zai-org-GLM-5.2.json @@ -0,0 +1,1622 @@ +[ + { + "dataset": "agnews", + "query": "1", + "run": "0", + "answer": "The Rundown" + }, + { + "dataset": "agnews", + "query": "1", + "run": "1", + "answer": "The Rundown" + }, + { + "dataset": "agnews", + "query": "1", + "run": "2", + "answer": "The Rundown" + }, + { + "dataset": "agnews", + "query": "1", + "run": "3", + "answer": "The Rundown" + }, + { + "dataset": "agnews", + "query": "1", + "run": "4", + "answer": "The Rundown" + }, + { + "dataset": "agnews", + "query": "2", + "run": "0", + "answer": "25/111" + }, + { + "dataset": "agnews", + "query": "2", + "run": "1", + "answer": "1.0" + }, + { + "dataset": "agnews", + "query": "2", + "run": "2", + "answer": "0.14414414414414414" + }, + { + "dataset": "agnews", + "query": "2", + "run": "3", + "answer": "0.1441" + }, + { + "dataset": "agnews", + "query": "2", + "run": "4", + "answer": "19/111" + }, + { + "dataset": "agnews", + "query": "3", + "run": "0", + "answer": "338" + }, + { + "dataset": "agnews", + "query": "3", + "run": "1", + "answer": "The boundary analysis confirms the pattern repeats in each 30k block:\n- **World**: 0-7499, 30000-37499, 60000-67499, 90000-97499, 120000-127599\n- **Sports**: 7500-14999, 37500-44999, 67500-74999, 97500-104999\n- **Business**: 15000-22499, 45000-52499, 75000-82499, 105000-112499\n- **Sci/Tech**: 22500-29999, 52500-59999, 82500-89999, 112500-119999\n\nLet me verify these boundaries by sampling the transition points." + }, + { + "dataset": "agnews", + "query": "3", + "run": "2", + "answer": "The regions are evenly distributed across all article_id modulo-5 groups, confirming region is independent of category. Now let me verify whether AG News categories follow a repeating block structure to determine the business articles efficiently. Let me check if there's a periodic pattern by sampling article_ids at different mod values." + }, + { + "dataset": "agnews", + "query": "3", + "run": "3", + "answer": "338" + }, + { + "dataset": "agnews", + "query": "3", + "run": "4", + "answer": "337.73" + }, + { + "dataset": "agnews", + "query": "4", + "run": "0", + "answer": "Europe" + }, + { + "dataset": "agnews", + "query": "4", + "run": "1", + "answer": "Africa" + }, + { + "dataset": "agnews", + "query": "4", + "run": "2", + "answer": "Europe" + }, + { + "dataset": "agnews", + "query": "4", + "run": "3", + "answer": "South America" + }, + { + "dataset": "agnews", + "query": "4", + "run": "4", + "answer": "Europe" + }, + { + "dataset": "bookreview", + "query": "1", + "run": "0", + "answer": "2020s" + }, + { + "dataset": "bookreview", + "query": "1", + "run": "1", + "answer": "2020s" + }, + { + "dataset": "bookreview", + "query": "1", + "run": "2", + "answer": "The round_down transform malfunctioned on the SQL-extracted integer (giving 20/10 instead of 2020/2010). I verified my manual weighted average calculation (523/112 = 4.6696) confirms the group_avg IS correctly weighting by review count \u2014 my earlier \"discrepancy\" was a faulty manual test. Now let me run the definitive query with the correct date-extraction priority (subtitle first \u2192 details \u2192 title fallback) to properly classify books like 166 (2005) and 174 (1990) whose dates only appear in the title." + }, + { + "dataset": "bookreview", + "query": "1", + "run": "3", + "answer": "2020s" + }, + { + "dataset": "bookreview", + "query": "1", + "run": "4", + "answer": "2020s" + }, + { + "dataset": "bookreview", + "query": "2", + "run": "0", + "answer": "Reunion: The Children of Lauderdale Park\nThe Prophet: With Original 1923 Illustrations by the Author\nThe Melancholy Strumpet Master\nChild Of The King A Journey of Hope Book 1: Earthly Story With A Heavenly Message\nFire Cracker\nLocal Honey\nHollywood Confessions: Hollywood Headlines Book #3 (Hollywood Headlines Mysteries)\nKnowing When To Die: Uncollected Stories\nChilde Harold of Dysna\nForged in Blood (Freehold)\nExits, Desires, & Slow Fires\nKennebago Moments\nThe Sludge\nLiza of Lambeth\nSomething That Feels Like Truth (Switchgrass Books)" + }, + { + "dataset": "bookreview", + "query": "2", + "run": "1", + "answer": "Child Of The King A Journey of Hope Book 1: Earthly Story With A Heavenly Message\nChilde Harold of Dysna\nExits, Desires, & Slow Fires\nFire Cracker\nForged in Blood (Freehold)\nHollywood Confessions: Hollywood Headlines Book #3 (Hollywood Headlines Mysteries)\nKennebago Moments\nKnowing When To Die: Uncollected Stories\nLiza of Lambeth\nLocal Honey\nReunion: The Children of Lauderdale Park\nSomething That Feels Like Truth (Switchgrass Books)\nThe Melancholy Strumpet Master\nThe Prophet: With Original 1923 Illustrations by the Author\nThe Sludge" + }, + { + "dataset": "bookreview", + "query": "2", + "run": "2", + "answer": "Reunion: The Children of Lauderdale Park\nThe Prophet: With Original 1923 Illustrations by the Author\nThe Melancholy Strumpet Master\nChild Of The King A Journey of Hope Book 1: Earthly Story With A Heavenly Message\nFire Cracker\nLocal Honey\nHollywood Confessions: Hollywood Headlines Book #3 (Hollywood Headlines Mysteries)\nKnowing When To Die: Uncollected Stories\nChilde Harold of Dysna\nForged in Blood (Freehold)\nExits, Desires, & Slow Fires\nKennebago Moments\nThe Sludge\nLiza of Lambeth\nSomething That Feels Like Truth (Switchgrass Books)" + }, + { + "dataset": "bookreview", + "query": "2", + "run": "3", + "answer": "Child Of The King A Journey of Hope Book 1: Earthly Story With A Heavenly Message\nChilde Harold of Dysna\nExits, Desires, & Slow Fires\nFire Cracker\nForged in Blood (Freehold)\nHollywood Confessions: Hollywood Headlines Book #3 (Hollywood Headlines Mysteries)\nKennebago Moments\nKnowing When To Die: Uncollected Stories\nLiza of Lambeth\nLocal Honey\nReunion: The Children of Lauderdale Park\nSomething That Feels Like Truth (Switchgrass Books)\nThe Melancholy Strumpet Master\nThe Prophet: With Original 1923 Illustrations by the Author\nThe Sludge" + }, + { + "dataset": "bookreview", + "query": "2", + "run": "4", + "answer": "Reunion: The Children of Lauderdale Park\nThe Prophet: With Original 1923 Illustrations by the Author\nThe Melancholy Strumpet Master\nChild Of The King A Journey of Hope Book 1: Earthly Story With A Heavenly Message\nFire Cracker\nLocal Honey\nHollywood Confessions: Hollywood Headlines Book #3 (Hollywood Headlines Mysteries)\nKnowing When To Die: Uncollected Stories\nChilde Harold of Dysna\nForged in Blood (Freehold)\nExits, Desires, & Slow Fires\nKennebago Moments\nThe Sludge\nLiza of Lambeth\nSomething That Feels Like Truth (Switchgrass Books)" + }, + { + "dataset": "bookreview", + "query": "3", + "run": "0", + "answer": "Monstrous Stories #4: The Day the Mice Stood Still\nThe Old Man and the Pirate Princess\nEgypt (Enchantment of the World)\nFavorite Thorton W. Burgess Stories: 6 Books\nBehind the Wheel (Choose Your Own Adventure #35)(Paperback/Revised)\nCheer Up, Ben Franklin! (Young Historians)\nThe Library Book\nBenny Goes To The Moon: The great new book from Top Children's entertainer Gerry Ogilvie (1)\nLunaLu the Llamacorn\nAround the World Mazes\nPok\u00e9mon: Sun & Moon, Vol. 8 (8)\nTrouble in the CTC!: The Terra Prime Adventures Book 2\nClark the Shark: Tooth Trouble, No. 1\nCleo Porter and the Body Electric" + }, + { + "dataset": "bookreview", + "query": "3", + "run": "1", + "answer": "Around the World Mazes\nBehind the Wheel (Choose Your Own Adventure #35)(Paperback/Revised)\nBenny Goes To The Moon: The great new book from Top Children's entertainer Gerry Ogilvie (1)\nCheer Up, Ben Franklin! (Young Historians)\nClark the Shark: Tooth Trouble, No. 1\nCleo Porter and the Body Electric\nEgypt (Enchantment of the World)\nFavorite Thorton W. Burgess Stories: 6 Books\nLunaLu the Llamacorn\nMonstrous Stories #4: The Day the Mice Stood Still\nPok\u00e9mon: Sun & Moon, Vol. 8 (8)\nThe Library Book\nThe Old Man and the Pirate Princess\nTrouble in the CTC!: The Terra Prime Adventures Book 2" + }, + { + "dataset": "bookreview", + "query": "3", + "run": "2", + "answer": "Monstrous Stories #4: The Day the Mice Stood Still\nThe Old Man and the Pirate Princess\nEgypt (Enchantment of the World)\nFavorite Thorton W. Burgess Stories: 6 Books\nBehind the Wheel (Choose Your Own Adventure #35)(Paperback/Revised)\nCheer Up, Ben Franklin! (Young Historians)\nThe Library Book\nBenny Goes To The Moon: The great new book from Top Children's entertainer Gerry Ogilvie (1)\nLunaLu the Llamacorn\nAround the World Mazes\nPok\u00e9mon: Sun & Moon, Vol. 8 (8)\nTrouble in the CTC!: The Terra Prime Adventures Book 2\nClark the Shark: Tooth Trouble, No. 1\nCleo Porter and the Body Electric" + }, + { + "dataset": "bookreview", + "query": "3", + "run": "3", + "answer": "Around the World Mazes\nBehind the Wheel (Choose Your Own Adventure #35)(Paperback/Revised)\nBenny Goes To The Moon: The great new book from Top Children's entertainer Gerry Ogilvie (1)\nCheer Up, Ben Franklin! (Young Historians)\nClark the Shark: Tooth Trouble, No. 1\nCleo Porter and the Body Electric\nEgypt (Enchantment of the World)\nFavorite Thorton W. Burgess Stories: 6 Books\nLunaLu the Llamacorn\nMonstrous Stories #4: The Day the Mice Stood Still\nPok\u00e9mon: Sun & Moon, Vol. 8 (8)\nThe Library Book\nThe Old Man and the Pirate Princess\nTrouble in the CTC!: The Terra Prime Adventures Book 2" + }, + { + "dataset": "bookreview", + "query": "3", + "run": "4", + "answer": "Monstrous Stories #4: The Day the Mice Stood Still\nThe Old Man and the Pirate Princess\nEgypt (Enchantment of the World)\nFavorite Thorton W. Burgess Stories: 6 Books\nBehind the Wheel (Choose Your Own Adventure #35)(Paperback/Revised)\nCheer Up, Ben Franklin! (Young Historians)\nThe Library Book\nBenny Goes To The Moon: The great new book from Top Children's entertainer Gerry Ogilvie (1)\nLunaLu the Llamacorn\nAround the World Mazes\nPok\u00e9mon: Sun & Moon, Vol. 8 (8)\nTrouble in the CTC!: The Terra Prime Adventures Book 2\nClark the Shark: Tooth Trouble, No. 1\nCleo Porter and the Body Electric" + }, + { + "dataset": "crmarenapro", + "query": "1", + "run": "0", + "answer": "Authority" + }, + { + "dataset": "crmarenapro", + "query": "1", + "run": "1", + "answer": "Authority\nTimeline" + }, + { + "dataset": "crmarenapro", + "query": "1", + "run": "2", + "answer": "Authority" + }, + { + "dataset": "crmarenapro", + "query": "1", + "run": "3", + "answer": "Authority" + }, + { + "dataset": "crmarenapro", + "query": "1", + "run": "4", + "answer": "Authority" + }, + { + "dataset": "crmarenapro", + "query": "10", + "run": "0", + "answer": "005Wt000003NDqDIAW" + }, + { + "dataset": "crmarenapro", + "query": "10", + "run": "1", + "answer": "005Wt000003NDqDIAW" + }, + { + "dataset": "crmarenapro", + "query": "10", + "run": "2", + "answer": "005Wt000003NDqDIAW" + }, + { + "dataset": "crmarenapro", + "query": "10", + "run": "3", + "answer": "005Wt000003NDqDIAW" + }, + { + "dataset": "crmarenapro", + "query": "10", + "run": "4", + "answer": "005Wt000003NDqDIAW" + }, + { + "dataset": "crmarenapro", + "query": "11", + "run": "0", + "answer": "01tWt000006hV8LIAU" + }, + { + "dataset": "crmarenapro", + "query": "11", + "run": "1", + "answer": "01tWt000006hV8LIAU" + }, + { + "dataset": "crmarenapro", + "query": "11", + "run": "2", + "answer": "01tWt000006hV8LIAU" + }, + { + "dataset": "crmarenapro", + "query": "11", + "run": "3", + "answer": "01tWt000006hV8LIAU" + }, + { + "dataset": "crmarenapro", + "query": "11", + "run": "4", + "answer": "01tWt000006hV8LIAU" + }, + { + "dataset": "crmarenapro", + "query": "12", + "run": "0", + "answer": "005Wt000003NJgAIAW" + }, + { + "dataset": "crmarenapro", + "query": "12", + "run": "1", + "answer": "005Wt000003NJgAIAW" + }, + { + "dataset": "crmarenapro", + "query": "12", + "run": "2", + "answer": "#005Wt000003NEa3IAG" + }, + { + "dataset": "crmarenapro", + "query": "12", + "run": "3", + "answer": "005Wt000003NJgAIAW" + }, + { + "dataset": "crmarenapro", + "query": "12", + "run": "4", + "answer": "005Wt000003NJgAIAW" + }, + { + "dataset": "crmarenapro", + "query": "13", + "run": "0", + "answer": "#005Wt000003NHw5IAG" + }, + { + "dataset": "crmarenapro", + "query": "13", + "run": "1", + "answer": "005Wt000003NHw5IAG" + }, + { + "dataset": "crmarenapro", + "query": "13", + "run": "2", + "answer": "#005Wt000003NHw5IAG" + }, + { + "dataset": "crmarenapro", + "query": "13", + "run": "3", + "answer": "005Wt000003NHw5IAG" + }, + { + "dataset": "crmarenapro", + "query": "13", + "run": "4", + "answer": "005Wt000003NIXCIA4" + }, + { + "dataset": "crmarenapro", + "query": "2", + "run": "0", + "answer": "ka0Wt000000Ens5IAC" + }, + { + "dataset": "crmarenapro", + "query": "2", + "run": "1", + "answer": "ka0Wt000000Ens5IAC" + }, + { + "dataset": "crmarenapro", + "query": "2", + "run": "2", + "answer": "ka0Wt000000Ens5IAC" + }, + { + "dataset": "crmarenapro", + "query": "2", + "run": "3", + "answer": "ka0Wt000000Eq0MIAS" + }, + { + "dataset": "crmarenapro", + "query": "2", + "run": "4", + "answer": "ka0Wt000000Ens5IAC" + }, + { + "dataset": "crmarenapro", + "query": "3", + "run": "0", + "answer": "Quote" + }, + { + "dataset": "crmarenapro", + "query": "3", + "run": "1", + "answer": "Negotiation" + }, + { + "dataset": "crmarenapro", + "query": "3", + "run": "2", + "answer": "Quote" + }, + { + "dataset": "crmarenapro", + "query": "3", + "run": "3", + "answer": "Quote" + }, + { + "dataset": "crmarenapro", + "query": "3", + "run": "4", + "answer": "Quote" + }, + { + "dataset": "crmarenapro", + "query": "4", + "run": "0", + "answer": "November" + }, + { + "dataset": "crmarenapro", + "query": "4", + "run": "1", + "answer": "November" + }, + { + "dataset": "crmarenapro", + "query": "4", + "run": "2", + "answer": "November" + }, + { + "dataset": "crmarenapro", + "query": "4", + "run": "3", + "answer": "November" + }, + { + "dataset": "crmarenapro", + "query": "4", + "run": "4", + "answer": "November" + }, + { + "dataset": "crmarenapro", + "query": "5", + "run": "0", + "answer": "a03Wt00000JqnHwIAJ" + }, + { + "dataset": "crmarenapro", + "query": "5", + "run": "1", + "answer": "a03Wt00000JqnHwIAJ" + }, + { + "dataset": "crmarenapro", + "query": "5", + "run": "2", + "answer": "a03Wt00000JqnHwIAJ" + }, + { + "dataset": "crmarenapro", + "query": "5", + "run": "3", + "answer": "a03Wt00000JqnHwIAJ" + }, + { + "dataset": "crmarenapro", + "query": "5", + "run": "4", + "answer": "a03Wt00000JqnHwIAJ" + }, + { + "dataset": "crmarenapro", + "query": "6", + "run": "0", + "answer": "ka0Wt000000EnwvIAC" + }, + { + "dataset": "crmarenapro", + "query": "6", + "run": "1", + "answer": "ka0Wt000000EnwvIAC" + }, + { + "dataset": "crmarenapro", + "query": "6", + "run": "2", + "answer": "#ka0Wt000000EnwvIAC" + }, + { + "dataset": "crmarenapro", + "query": "6", + "run": "3", + "answer": "#ka0Wt000000EnwvIAC" + }, + { + "dataset": "crmarenapro", + "query": "6", + "run": "4", + "answer": "ka0Wt000000Ens5IAC" + }, + { + "dataset": "crmarenapro", + "query": "7", + "run": "0", + "answer": "ka0Wt000000EoD3IAK" + }, + { + "dataset": "crmarenapro", + "query": "7", + "run": "1", + "answer": "ka0Wt000000EoD3IAK" + }, + { + "dataset": "crmarenapro", + "query": "7", + "run": "2", + "answer": "#ka0Wt000000EpSUIA0" + }, + { + "dataset": "crmarenapro", + "query": "7", + "run": "3", + "answer": "ka0Wt000000EpSUIA0" + }, + { + "dataset": "crmarenapro", + "query": "7", + "run": "4", + "answer": "ka0Wt000000EoD3IAK" + }, + { + "dataset": "crmarenapro", + "query": "8", + "run": "0", + "answer": "` tags.\n\n\n005Wt000003NDqFIAW" + }, + { + "dataset": "crmarenapro", + "query": "8", + "run": "1", + "answer": "I found the issue - 44 of 153 Case IDs have a leading '#' but casehistory IDs don't. Let me check casehistory caseid__c for '#' corruption and then redo the analysis with proper corruption handling." + }, + { + "dataset": "crmarenapro", + "query": "8", + "run": "2", + "answer": "005Wt000003NFKoIAO" + }, + { + "dataset": "crmarenapro", + "query": "8", + "run": "3", + "answer": "005Wt000003NIliIAG" + }, + { + "dataset": "crmarenapro", + "query": "8", + "run": "4", + "answer": "005Wt000003NFKoIAO" + }, + { + "dataset": "crmarenapro", + "query": "9", + "run": "0", + "answer": "MI" + }, + { + "dataset": "crmarenapro", + "query": "9", + "run": "1", + "answer": "MI" + }, + { + "dataset": "crmarenapro", + "query": "9", + "run": "2", + "answer": "IA" + }, + { + "dataset": "crmarenapro", + "query": "9", + "run": "3", + "answer": "MI" + }, + { + "dataset": "crmarenapro", + "query": "9", + "run": "4", + "answer": "TX" + }, + { + "dataset": "deps_dev_v1", + "query": "1", + "run": "0", + "answer": "The result is capped at 1000 rows. Since I have 15,811 distinct packages, I can't retrieve all release rows. I need to be smarter \u2014 get only the latest version per package. Let me try using a spec approach where sub-query 1 returns (Name, max_ordinal) and sub-query 2 returns (Name, Version, Ordinal), then I filter via transform. But the 1000-row cap applies per sub-query.\n\nLet me think differently. I'll combine the max_ordinal extraction and version retrieval using a JOIN via the spec format with two sqlite sub-queries:" + }, + { + "dataset": "deps_dev_v1", + "query": "1", + "run": "1", + "answer": "lodash 57779, leaflet 38715, mapbox-gl-js 10348, react-native-swiper 10249, gremlins.js 8973" + }, + { + "dataset": "deps_dev_v1", + "query": "1", + "run": "2", + "answer": "I have the top 5 projects by stars. Now I need the package names for these 5 projects and their latest release versions. Let me get the distinct names for the top 5 projects, then query SQLite for the latest release version of each." + }, + { + "dataset": "deps_dev_v1", + "query": "1", + "run": "3", + "answer": "The result has a flaw: the SQLite subquery was truncated at 1000 rows, causing a fan-out issue in the join (cross-join effect). I need to fix this. The cleanest approach: aggregate within DuckDB first (rank packages per project by stars, dedupe projects), then join.\n\nLet me test star extraction and count all projects with non-null stars." + }, + { + "dataset": "deps_dev_v1", + "query": "1", + "run": "4", + "answer": "I have one turn left. The key unknown: do `packageinfo` names (all `@`-scoped, clean) match `project_packageversion` names (which include nested paths like `@dollarshaveclub/cli>1.0.0>lodash` AND clean scoped names)? Let me run two parallel diagnostics: (1) whether packageinfo has `>` names, and (2) which top-star projects have clean (non-nested) package names in project_packageversion." + }, + { + "dataset": "deps_dev_v1", + "query": "2", + "run": "0", + "answer": "The query is still matching. Let me adjust the WHERE clause." + }, + { + "dataset": "deps_dev_v1", + "query": "2", + "run": "1", + "answer": "I need to understand the data formats first. Let me sample the relevant fields in parallel." + }, + { + "dataset": "deps_dev_v1", + "query": "2", + "run": "2", + "answer": "mui-org/material-ui\nmoment/moment\nsemantic-org/semantic-ui\nreact-native-elements/react-native-elements\nsveltejs/svelte" + }, + { + "dataset": "deps_dev_v1", + "query": "2", + "run": "3", + "answer": "mui-org/material-ui\nsemantic-org/semantic-ui\nreact-native-elements/react-native-elements\nsveltejs/svelte\ntailwindcss/tailwindcss" + }, + { + "dataset": "deps_dev_v1", + "query": "2", + "run": "4", + "answer": "mui-org/material-ui\nsemantic-org/semantic-ui\nreact-native-elements/react-native-elements\nsveltejs/svelte\ntailwindcss/tailwindcss" + }, + { + "dataset": "github_repos", + "query": "1", + "run": "0", + "answer": "19/111 (approximately 0.1712 or 17.12%)" + }, + { + "dataset": "github_repos", + "query": "1", + "run": "1", + "answer": "11.11%" + }, + { + "dataset": "github_repos", + "query": "1", + "run": "2", + "answer": "12.32%" + }, + { + "dataset": "github_repos", + "query": "1", + "run": "3", + "answer": "15.24%" + }, + { + "dataset": "github_repos", + "query": "1", + "run": "4", + "answer": "Let me correct the spec format." + }, + { + "dataset": "github_repos", + "query": "2", + "run": "0", + "answer": "Mozharovsky/CVCalendar" + }, + { + "dataset": "github_repos", + "query": "2", + "run": "1", + "answer": "uacaps/PageMenu" + }, + { + "dataset": "github_repos", + "query": "2", + "run": "2", + "answer": "uacaps/PageMenu" + }, + { + "dataset": "github_repos", + "query": "2", + "run": "3", + "answer": "uacaps/PageMenu" + }, + { + "dataset": "github_repos", + "query": "2", + "run": "4", + "answer": "uacaps/PageMenu" + }, + { + "dataset": "github_repos", + "query": "3", + "run": "0", + "answer": "1077" + }, + { + "dataset": "github_repos", + "query": "3", + "run": "1", + "answer": "0" + }, + { + "dataset": "github_repos", + "query": "3", + "run": "2", + "answer": "1077" + }, + { + "dataset": "github_repos", + "query": "3", + "run": "3", + "answer": "1077" + }, + { + "dataset": "github_repos", + "query": "3", + "run": "4", + "answer": "1077" + }, + { + "dataset": "github_repos", + "query": "4", + "run": "0", + "answer": "apple/swift\ntwbs/bootstrap\nMicrosoft/vscode\nfacebook/react\ntensorflow/tensorflow" + }, + { + "dataset": "github_repos", + "query": "4", + "run": "1", + "answer": "apple/swift\ntwbs/bootstrap\nMicrosoft/vscode\nfacebook/react\ntensorflow/tensorflow" + }, + { + "dataset": "github_repos", + "query": "4", + "run": "2", + "answer": "apple/swift\ntwbs/bootstrap\nMicrosoft/vscode\nfacebook/react\ntensorflow/tensorflow" + }, + { + "dataset": "github_repos", + "query": "4", + "run": "3", + "answer": "apple/swift\ntwbs/bootstrap\nMicrosoft/vscode\nfacebook/react\ntensorflow/tensorflow" + }, + { + "dataset": "github_repos", + "query": "4", + "run": "4", + "answer": "apple/swift\ntwbs/bootstrap\nMicrosoft/vscode\nfacebook/react\ntensorflow/tensorflow" + }, + { + "dataset": "googlelocal", + "query": "1", + "run": "0", + "answer": "Widows Peak Salon\nCity Textile\nNobel Textile Co\nSan Soo Dang\nNova Fabrics" + }, + { + "dataset": "googlelocal", + "query": "1", + "run": "1", + "answer": "Widows Peak Salon\nCity Textile\nNobel Textile Co\nSan Soo Dang\nNova Fabrics" + }, + { + "dataset": "googlelocal", + "query": "1", + "run": "2", + "answer": "Widows Peak Salon\nCity Textile\nNobel Textile Co\nSan Soo Dang\nNova Fabrics" + }, + { + "dataset": "googlelocal", + "query": "1", + "run": "3", + "answer": "Widows Peak Salon (4.857)\nCity Textile (4.5)\nNobel Textile Co (4.286)\nSan Soo Dang (4.278)\nNova Fabrics (3.333)" + }, + { + "dataset": "googlelocal", + "query": "1", + "run": "4", + "answer": "Widows Peak Salon\nCity Textile\nNobel Textile Co\nSan Soo Dang\nNova Fabrics" + }, + { + "dataset": "googlelocal", + "query": "2", + "run": "0", + "answer": "Elite Massage: 5.0\nAngel-A Massage: 4.33\nAurora Massage: 4.18" + }, + { + "dataset": "googlelocal", + "query": "2", + "run": "1", + "answer": "Angel-A Massage: 4.33\nAurora Massage: 4.18\nElite Massage: 5.00" + }, + { + "dataset": "googlelocal", + "query": "2", + "run": "2", + "answer": "Elite Massage, 5.0\nAngel-A Massage, 4.33\nAurora Massage, 4.18" + }, + { + "dataset": "googlelocal", + "query": "2", + "run": "3", + "answer": "Angel-A Massage, 4.33\nElite Massage, 5.0\nAurora Massage, 4.18" + }, + { + "dataset": "googlelocal", + "query": "2", + "run": "4", + "answer": "Elite Massage: 5.00\nAngel-A Massage: 4.33\nAurora Massage: 4.18\nJ B Oriental Inc: 4.17" + }, + { + "dataset": "googlelocal", + "query": "3", + "run": "0", + "answer": "White Barn Candle Co \u2014 Thursday: 10AM\u20139PM, Friday: 10AM\u20139PM, Saturday: 10AM\u20139PM, Sunday: 11AM\u20137PM, Monday: 10AM\u20139PM, Tuesday: 10AM\u20139PM, Wednesday: 10AM\u20139PM \u2014 5.0\nBeauty Divine Artistry \u2014 Thursday: 9AM\u20138PM, Friday: 9AM\u20138PM, Saturday: 10AM\u20137PM, Sunday: 11AM\u20136PM, Monday: 9AM\u20138PM, Tuesday: 9AM\u20138PM, Wednesday: 9AM\u20138PM \u2014 5.0\nTaba Rug Gallery \u2014 Thursday: 10AM\u20137PM, Friday: 10AM\u20137PM, Saturday: 10AM\u20137PM, Sunday: 11AM\u20136PM, Monday: 10AM\u20137PM, Tuesday: 10AM\u20137PM, Wednesday: 10AM\u20137PM \u2014 5.0\nTACOS LA CABANA \u2014 Thursday: Closed, Friday: 5\u201311PM, Saturday: 5\u201311PM, Sunday: 5\u201311PM, Monday: 5\u201311PM, Tuesday: Closed, Wednesday: Closed \u2014 5.0\nMariscos el poblano \u2014 Thursday: Open 24 hours, Friday: 8AM\u20133:30PM, Saturday: 8AM\u20133:30PM, Sunday: 8AM\u20133:30PM, Monday: 9AM\u20133:30AM, Tuesday: 8AM\u20133:30PM, Wednesday: 8AM\u20133:30PM \u2014 5.0" + }, + { + "dataset": "googlelocal", + "query": "3", + "run": "1", + "answer": "1. Mariscos el poblano \u2014 Thursday: Open 24 hours, Friday: 8AM\u20133:30PM, Saturday: 8AM\u20133:30PM, Sunday: 8AM\u20133:30PM, Monday: 9AM\u20133:30AM, Tuesday: 8AM\u20133:30PM, Wednesday: 8AM\u20133:30PM \u2014 Average rating: 5.0 (3 reviews)\n\n2. Beauty Divine Artistry \u2014 Thursday: 9AM\u20138PM, Friday: 9AM\u20138PM, Saturday: 10AM\u20137PM, Sunday: 11AM\u20136PM, Monday: 9AM\u20138PM, Tuesday: 9AM\u20138PM, Wednesday: 9AM\u20138PM \u2014 Average rating: 5.0 (8 reviews)\n\n3. White Barn Candle Co \u2014 Thursday: 10AM\u20139PM, Friday: 10AM\u20139PM, Saturday: 10AM\u20139PM, Sunday: 11AM\u20137PM, Monday: 10AM\u20139PM, Tuesday: 10AM\u20139PM, Wednesday: 10AM\u20139PM \u2014 Average rating: 5.0 (2 reviews)\n\n4. TACOS LA CABANA \u2014 Thursday: Closed, Friday: 5\u201311PM, Saturday: 5\u201311PM, Sunday: 5\u201311PM, Monday: 5\u201311PM, Tuesday: Closed, Wednesday: Closed \u2014 Average rating: 5.0 (2 reviews)\n\n5. Taba Rug Gallery \u2014 Thursday: 10AM\u20137PM, Friday: 10AM\u20137PM, Saturday: 10AM\u20137PM, Sunday: 11AM\u20136PM, Monday: 10AM\u20137PM, Tuesday: 10AM\u20137PM, Wednesday: 10AM\u20137PM \u2014 Average rating: 5.0 (18 reviews)" + }, + { + "dataset": "googlelocal", + "query": "3", + "run": "2", + "answer": "1. Mariscos el poblano \u2014 Thursday: Open 24 hours; Friday: 8AM\u20133:30PM; Saturday: 8AM\u20133:30PM; Sunday: 8AM\u20133:30PM; Monday: 9AM\u20133:30AM; Tuesday: 8AM\u20133:30PM; Wednesday: 8AM\u20133:30PM \u2014 Average rating: 5.0\n2. TACOS LA CABANA \u2014 Thursday: Closed; Friday: 5\u201311PM; Saturday: 5\u201311PM; Sunday: 5\u201311PM; Monday: 5\u201311PM; Tuesday: Closed; Wednesday: Closed \u2014 Average rating: 5.0\n3. Taba Rug Gallery \u2014 Thursday: 10AM\u20137PM; Friday: 10AM\u20137PM; Saturday: 10AM\u20137PM; Sunday: 11AM\u20136PM; Monday: 10AM\u20137PM; Tuesday: 10AM\u20137PM; Wednesday: 10AM\u20137PM \u2014 Average rating: 5.0\n4. Beauty Divine Artistry \u2014 Thursday: 9AM\u20138PM; Friday: 9AM\u20138PM; Saturday: 10AM\u20137PM; Sunday: 11AM\u20136PM; Monday: 9AM\u20138PM; Tuesday: 9AM\u20138PM; Wednesday: 9AM\u20138PM \u2014 Average rating: 5.0\n5. White Barn Candle Co \u2014 Thursday: 10AM\u20139PM; Friday: 10AM\u20139PM; Saturday: 10AM\u20139PM; Sunday: 11AM\u20137PM; Monday: 10AM\u20139PM; Tuesday: 10AM\u20139PM; Wednesday: 10AM\u20139PM \u2014 Average rating: 5.0" + }, + { + "dataset": "googlelocal", + "query": "3", + "run": "3", + "answer": "1. Taba Rug Gallery | Mon\u2013Fri 10AM\u20137PM, Sat 10AM\u20137PM, Sun 11AM\u20136PM | Average rating: 5.0\n2. Beauty Divine Artistry | Mon\u2013Fri 9AM\u20138PM, Sat 10AM\u20137PM, Sun 11AM\u20136PM | Average rating: 5.0\n3. White Barn Candle Co | Mon\u2013Sat 10AM\u20139PM, Sun 11AM\u20137PM | Average rating: 5.0\n4. TACOS LA CABANA | Fri\u2013Mon 5\u201311PM, Tue\u2013Thu Closed | Average rating: 5.0\n5. Mariscos el poblano | Thu Open 24 hours, Mon 9AM\u20133:30AM, Tue/Wed/Fri/Sat/Sun 8AM\u20133:30PM | Average rating: 5.0" + }, + { + "dataset": "googlelocal", + "query": "3", + "run": "4", + "answer": "1. Taba Rug Gallery \u2014 Average Rating: 5.0 \u2014 Hours: Thursday 10AM\u20137PM, Friday 10AM\u20137PM, Saturday 10AM\u20137PM, Sunday 11AM\u20136PM, Monday 10AM\u20137PM, Tuesday 10AM\u20137PM, Wednesday 10AM\u20137PM\n2. Beauty Divine Artistry \u2014 Average Rating: 5.0 \u2014 Hours: Thursday 9AM\u20138PM, Friday 9AM\u20138PM, Saturday 10AM\u20137PM, Sunday 11AM\u20136PM, Monday 9AM\u20138PM, Tuesday 9AM\u20138PM, Wednesday 9AM\u20138PM\n3. Mariscos el poblano \u2014 Average Rating: 5.0 \u2014 Hours: Thursday Open 24 hours, Friday 8AM\u20133:30PM, Saturday 8AM\u20133:30PM, Sunday 8AM\u20133:30PM, Monday 9AM\u20133:30AM, Tuesday 8AM\u20133:30PM, Wednesday 8AM\u20133:30PM\n4. White Barn Candle Co \u2014 Average Rating: 5.0 \u2014 Hours: Thursday 10AM\u20139PM, Friday 10AM\u20139PM, Saturday 10AM\u20139PM, Sunday 11AM\u20137PM, Monday 10AM\u20139PM, Tuesday 10AM\u20139PM, Wednesday 10AM\u20139PM\n5. TACOS LA CABANA \u2014 Average Rating: 5.0 \u2014 Hours: Thursday Closed, Friday 5\u201311PM, Saturday 5\u201311PM, Sunday 5\u201311PM, Monday 5\u201311PM, Tuesday Closed, Wednesday Closed" + }, + { + "dataset": "googlelocal", + "query": "4", + "run": "0", + "answer": "Encino Dermatology & Laser: Alex Khadavi MD: 19\nThe Boochyard @ Local Roots: 17\nAurora Massage: 14" + }, + { + "dataset": "googlelocal", + "query": "4", + "run": "1", + "answer": "Encino Dermatology & Laser: Alex Khadavi MD - 19\nThe Boochyard @ Local Roots - 17\nAurora Massage - 14" + }, + { + "dataset": "googlelocal", + "query": "4", + "run": "2", + "answer": "Encino Dermatology & Laser: Alex Khadavi MD: 19\nThe Boochyard @ Local Roots: 17\nAurora Massage: 14" + }, + { + "dataset": "googlelocal", + "query": "4", + "run": "3", + "answer": "Encino Dermatology & Laser: Alex Khadavi MD: 19\nThe Boochyard @ Local Roots: 17\nAurora Massage: 14" + }, + { + "dataset": "googlelocal", + "query": "4", + "run": "4", + "answer": "Encino Dermatology & Laser: Alex Khadavi MD, 19\nThe Boochyard @ Local Roots, 17\nAurora Massage, 14" + }, + { + "dataset": "music_brainz_20k", + "query": "1", + "run": "0", + "answer": "1059.46" + }, + { + "dataset": "music_brainz_20k", + "query": "1", + "run": "1", + "answer": "1059.46" + }, + { + "dataset": "music_brainz_20k", + "query": "1", + "run": "2", + "answer": "1059.46" + }, + { + "dataset": "music_brainz_20k", + "query": "1", + "run": "3", + "answer": "1059.46" + }, + { + "dataset": "music_brainz_20k", + "query": "1", + "run": "4", + "answer": "1059.46" + }, + { + "dataset": "music_brainz_20k", + "query": "2", + "run": "0", + "answer": "Amazon Music" + }, + { + "dataset": "music_brainz_20k", + "query": "2", + "run": "1", + "answer": "Amazon Music" + }, + { + "dataset": "music_brainz_20k", + "query": "2", + "run": "2", + "answer": "Amazon Music" + }, + { + "dataset": "music_brainz_20k", + "query": "2", + "run": "3", + "answer": "Amazon Music" + }, + { + "dataset": "music_brainz_20k", + "query": "2", + "run": "4", + "answer": "iTunes" + }, + { + "dataset": "music_brainz_20k", + "query": "3", + "run": "0", + "answer": "Systemisch bled" + }, + { + "dataset": "music_brainz_20k", + "query": "3", + "run": "1", + "answer": "Zo gaat het leven aan je voor" + }, + { + "dataset": "music_brainz_20k", + "query": "3", + "run": "2", + "answer": "Hooked on You" + }, + { + "dataset": "music_brainz_20k", + "query": "3", + "run": "3", + "answer": "Groovey by Rich Matteson" + }, + { + "dataset": "music_brainz_20k", + "query": "3", + "run": "4", + "answer": "Zo gaat het leven aan je voor" + }, + { + "dataset": "pancancer_atlas", + "query": "1", + "run": "0", + "answer": "LGG patients have RNASeq data but no IGF2 row specifically. Let me count how many of the 513 LGG barcodes actually have an IGF2 row." + }, + { + "dataset": "pancancer_atlas", + "query": "1", + "run": "1", + "answer": "Astrocytoma: 2.571298\nOligoastrocytoma: 2.713571\nOligodendroglioma: 2.682458" + }, + { + "dataset": "pancancer_atlas", + "query": "1", + "run": "2", + "answer": "Astrocytoma: 2.571298\nOligoastrocytoma: 2.713571\nOligodendroglioma: 2.682458" + }, + { + "dataset": "pancancer_atlas", + "query": "1", + "run": "3", + "answer": "no answer" + }, + { + "dataset": "pancancer_atlas", + "query": "1", + "run": "4", + "answer": "Astrocytoma: 2.5713\nOligodendroglioma: 2.6637\nOligoastrocytoma: 2.6937" + }, + { + "dataset": "pancancer_atlas", + "query": "2", + "run": "0", + "answer": "Infiltrating Lobular Carcinoma\nMixed Histology (please specify)\nOther specify" + }, + { + "dataset": "pancancer_atlas", + "query": "2", + "run": "1", + "answer": "Infiltrating Lobular Carcinoma\nMixed Histology (please specify)\nOther specify" + }, + { + "dataset": "pancancer_atlas", + "query": "2", + "run": "2", + "answer": "Infiltrating Lobular Carcinoma\nMixed Histology (please specify)\nOther specify" + }, + { + "dataset": "pancancer_atlas", + "query": "2", + "run": "3", + "answer": "Infiltrating Lobular Carcinoma (50.56%)\nMixed Histology (please specify) (16.67%)\nOther specify (8.33%)" + }, + { + "dataset": "pancancer_atlas", + "query": "2", + "run": "4", + "answer": "Infiltrating Lobular Carcinoma\nMixed Histology (please specify)\nOther specify" + }, + { + "dataset": "pancancer_atlas", + "query": "3", + "run": "0", + "answer": "305.1239198007461" + }, + { + "dataset": "pancancer_atlas", + "query": "3", + "run": "1", + "answer": "305.123919800746" + }, + { + "dataset": "pancancer_atlas", + "query": "3", + "run": "2", + "answer": "305.123919800746" + }, + { + "dataset": "pancancer_atlas", + "query": "3", + "run": "3", + "answer": "305.123919800746" + }, + { + "dataset": "pancancer_atlas", + "query": "3", + "run": "4", + "answer": "305.1239198007461" + }, + { + "dataset": "patents", + "query": "1", + "run": "0", + "answer": "Y02E\nG06V\nG06N\nY02P\nG16H\nB65G\nA24F\nB33Y\nY02B\nB08B\nC25B\nB60H\nE02D\nF16M\nF26B\nC22B\nE04G\nC01G\nA62C\nE01C\nB66F\nB07B\nA62B\nH02B\nB65F\nA63H\nE21D\nC12R\nB60P\nC21B\nE02B\nE01D\nG05G\nB21C\nA23J\nC21C\nA01P\nE03B\nG16Y\nE21F\nG01H\nB63G\nC01D\nB09C\nG16C\nB67C\nF04F\nB27M\nA41G\nD01G\nA41H\nA22B\nB27G\nH05F\nB27C\nA63D" + }, + { + "dataset": "patents", + "query": "1", + "run": "1", + "answer": "Y02E\nG06V\nG06N\nY02P\nG16H\nB65G\nA24F\nB33Y\nY02B\nB08B\nC25B\nB60H\nE02D\nF16M\nF26B\nC22B\nE04G\nC01G\nA62C\nE01C\nB66F\nB07B\nA62B\nH02B\nB65F\nA63H\nE21D\nC12R\nB60P\nC21B\nE02B\nE01D\nG05G\nB21C\nA23J\nC21C\nA01P\nE03B\nG16Y\nE21F\nG01H\nB63G\nF23N\nC01D\nB09C\nG16C\nB67C\nF04F\nA21C\nB27M\nA41G\nD01G\nA41H\nA22B\nF42C\nB27G\nH05F\nB27C\nA42C\nA63D\nB42B\nG06J\nF17B" + }, + { + "dataset": "patents", + "query": "1", + "run": "2", + "answer": "no answer" + }, + { + "dataset": "patents", + "query": "1", + "run": "3", + "answer": "Let me check the level-5 symbol lengths, the year range in filing dates, and verify that taking the first 4 characters of publication CPC codes maps to level-5 symbols." + }, + { + "dataset": "patents", + "query": "1", + "run": "4", + "answer": "A41G\nA41H\nB21C\nB60H\nC01D\nC01G\nC21C\nC22B\nD01G\nE01C\nE01D\nE02B\nG01H\nY02B\nY02E\nY02P" + }, + { + "dataset": "patents", + "query": "2", + "run": "0", + "answer": "MEDICAL OR VETERINARY SCIENCE; HYGIENE | A61 | 2016" + }, + { + "dataset": "patents", + "query": "2", + "run": "1", + "answer": "G01 | MEASURING; TESTING | 2014\nG06 | COMPUTING; CALCULATING OR COUNTING | 2016\nY02 | TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE | 2013\nY10 | TECHNICAL SUBJECTS COVERED BY FORMER USPC | 2012" + }, + { + "dataset": "patents", + "query": "2", + "run": "2", + "answer": "I see German patents use \"DE-\" prefix in application numbers, and level-4 CPC symbols are 3-character codes (like A01, B01, H01). Let me verify the CPC level structure and check the filing_date parsing." + }, + { + "dataset": "patents", + "query": "2", + "run": "3", + "answer": "MEASURING; TESTING | G01 | 2014\nCOMPUTING; CALCULATING OR COUNTING | G06 | 2016\nTECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE | Y02 | 2013\nTECHNICAL SUBJECTS COVERED BY FORMER USPC | Y10 | 2012" + }, + { + "dataset": "patents", + "query": "2", + "run": "4", + "answer": "A61 \u2014 MEDICAL OR VETERINARY SCIENCE; HYGIENE \u2014 best year 2016 (EMA 3.0)\nH04 \u2014 ELECTRIC COMMUNICATION TECHNIQUE \u2014 best year 2015 (EMA 2.0)\nA21 \u2014 BAKING; EDIBLE DOUGHS \u2014 best year 2015 (EMA 1.0)\nA43 \u2014 FOOTWEAR \u2014 best year 2016 (EMA 1.0)\nA47 \u2014 FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL \u2014 best year 2015 (EMA 1.0)\nB23 \u2014 MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR \u2014 best year 2015 (EMA 1.0)\nB29 \u2014 WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL \u2014 best year 2007 (EMA 1.0)\nB41 \u2014 PRINTING; LINING MACHINES; TYPEWRITERS; STAMPS \u2014 best year 2007 (EMA 1.0)\nB60 \u2014 VEHICLES IN GENERAL \u2014 best year 2009 (EMA 1.0)\nB62 \u2014 LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS \u2014 best year 2010 (EMA 1.0)\nB63 \u2014 SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT \u2014 best year 2014 (EMA 1.0)\nB64 \u2014 AIRCRAFT; AVIATION; COSMONAUTICS \u2014 best year 2018 (EMA 1.0)\nB66 \u2014 HOISTING; LIFTING; HAULING \u2014 best year 2016 (EMA 1.0)\nC04 \u2014 CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES \u2014 best year 2015 (EMA 1.0)\nC09 \u2014 DYES; PAINTS; POLISHES; NATURAL RESINS; ADHESIVES; COMPOSITIONS NOT OTHERWISE PROVIDED FOR; APPLICATIONS OF MATERIALS NOT OTHERWISE PROVIDED FOR \u2014 best year 2015 (EMA 1.0)\nE02 \u2014 HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING \u2014 best year 2012 (EMA 1.0)\nE05 \u2014 LOCKS; KEYS; WINDOW OR DOOR FITTINGS; SAFES \u2014 best year 2012 (EMA 1.0)\nF01 \u2014 MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES \u2014 best year 2018 (EMA 1.0)\nF02 \u2014 COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS \u2014 best year 2010 (EMA 1.0)\nF04 \u2014 POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS \u2014 best year 2014 (EMA 1.0)\nF05 \u2014 INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04 \u2014 best year 2018 (EMA 1.0)\nF16 \u2014 ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL \u2014 best year 2009 (EMA 1.0)\nF23 \u2014 COMBUSTION APPARATUS; COMBUSTION PROCESSES \u2014 best year 2018 (EMA 1.0)\nF24 \u2014 HEATING; RANGES; VENTILATING \u2014 best year 2018 (EMA 1.0)\nF41 \u2014 WEAPONS \u2014 best year 2012 (EMA 1.0)\nF42 \u2014 AMMUNITION; BLASTING \u2014 best year 2012 (EMA 1.0)\nG01 \u2014 MEASURING; TESTING \u2014 best year 2008 (EMA 1.0)\nG02 \u2014 OPTICS \u2014 best year 2016 (EMA 1.0)\nG07 \u2014 CHECKING-DEVICES \u2014 best year 2012 (EMA 1.0)\nG08 \u2014 SIGNALLING \u2014 best year 2017 (EMA 1.0)\nH01 \u2014 ELECTRIC ELEMENTS \u2014 best year 2008 (EMA 1.0)\nH02 \u2014 GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER \u2014 best year 2009 (EMA 1.0)\nH03 \u2014 ELECTRONIC CIRCUITRY \u2014 best year 2015 (EMA 1.0)\nY02 \u2014 TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE \u2014 best year 2013 (EMA 1.0)\nY10 \u2014 TECHNICAL SUBJECTS COVERED BY FORMER USPC \u2014 best year 2012 (EMA 1.0)" + }, + { + "dataset": "patents", + "query": "3", + "run": "0", + "answer": "CALIFORNIA INST OF TECHN \u2014 GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS\nBLOOM ENERGY CORP \u2014 PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY" + }, + { + "dataset": "patents", + "query": "3", + "run": "1", + "answer": "I found 2 citing patents. Now I need their CPC codes (to find primary CPC subclasses) and the cpc_definition titles. Let me query both databases in parallel." + }, + { + "dataset": "patents", + "query": "3", + "run": "2", + "answer": "BLOOM ENERGY CORP" + }, + { + "dataset": "patents", + "query": "3", + "run": "3", + "answer": "I have all 169 UC publication numbers. Now I need to find citing patents. Let me test an approach that joins UC pubs with citing patents via json_each:" + }, + { + "dataset": "patents", + "query": "3", + "run": "4", + "answer": "CALIFORNIA INST OF TECHN \u2014 GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS\nBLOOM ENERGY CORP \u2014 PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY\nCRYSTAL IS INC \u2014 SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR\nSCHOWALTER LEO J \u2014 SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10" + }, + { + "dataset": "stockindex", + "query": "1", + "run": "0", + "answer": "399001.SZ" + }, + { + "dataset": "stockindex", + "query": "1", + "run": "1", + "answer": "399001.SZ" + }, + { + "dataset": "stockindex", + "query": "1", + "run": "2", + "answer": "399001.SZ" + }, + { + "dataset": "stockindex", + "query": "1", + "run": "3", + "answer": "399001.SZ" + }, + { + "dataset": "stockindex", + "query": "1", + "run": "4", + "answer": "399001.SZ" + }, + { + "dataset": "stockindex", + "query": "2", + "run": "0", + "answer": "IXIC" + }, + { + "dataset": "stockindex", + "query": "2", + "run": "1", + "answer": "IXIC" + }, + { + "dataset": "stockindex", + "query": "2", + "run": "2", + "answer": "IXIC" + }, + { + "dataset": "stockindex", + "query": "2", + "run": "3", + "answer": "IXIC" + }, + { + "dataset": "stockindex", + "query": "2", + "run": "4", + "answer": "IXIC" + }, + { + "dataset": "stockindex", + "query": "3", + "run": "0", + "answer": "IXIC (NASDAQ Composite) \u2014 United States \u2014 367.70%\n399001.SZ (Shenzhen Component Index) \u2014 China \u2014 137.14%\nGDAXI (DAX) \u2014 Germany \u2014 135.58%\nTWII (TAIEX) \u2014 Taiwan \u2014 130.14%\nN225 (Nikkei 225) \u2014 Japan \u2014 112.34%" + }, + { + "dataset": "stockindex", + "query": "3", + "run": "1", + "answer": "IXIC \u2013 United States\nNSEI \u2013 India\n399001.SZ \u2013 China\nGDAXI \u2013 Germany\nTWII \u2013 Taiwan" + }, + { + "dataset": "stockindex", + "query": "3", + "run": "2", + "answer": "IXIC (United States), NSEI (India), 399001.SZ (China), GDAXI (Germany), TWII (Taiwan)" + }, + { + "dataset": "stockindex", + "query": "3", + "run": "3", + "answer": "IXIC (Nasdaq Composite) \u2013 United States \u2013 382.69%\nNSEI (Nifty 50) \u2013 India \u2013 135.84%\n399001.SZ (Shenzhen Component Index) \u2013 China \u2013 134.75%\nGDAXI (DAX) \u2013 Germany \u2013 134.70%\nTWII (Taiwan Capitalization Weighted Index) \u2013 Taiwan \u2013 129.82%" + }, + { + "dataset": "stockindex", + "query": "3", + "run": "4", + "answer": "1. IXIC (NASDAQ Composite) - United States - 367.53% return\n2. 399001.SZ (Shenzhen Component Index) - China - 136.78% return\n3. GDAXI (DAX) - Germany - 134.80% return\n4. NSEI (Nifty 50) - India - 134.11% return\n5. TWII (Taiwan Weighted Index) - Taiwan - 129.63% return" + }, + { + "dataset": "stockmarket", + "query": "1", + "run": "0", + "answer": "18.44" + }, + { + "dataset": "stockmarket", + "query": "1", + "run": "1", + "answer": "18.44" + }, + { + "dataset": "stockmarket", + "query": "1", + "run": "2", + "answer": "18.44" + }, + { + "dataset": "stockmarket", + "query": "1", + "run": "3", + "answer": "18.44" + }, + { + "dataset": "stockmarket", + "query": "1", + "run": "4", + "answer": "18.44" + }, + { + "dataset": "stockmarket", + "query": "2", + "run": "0", + "answer": "BOIL\nBZQ\nCOM\nDUST\nEDZ\nERX\nFAZ\nFXP\nGFIN\nGUSH\nHYUP\nJDST\nJNUG\nJPN\nLABD\nLABU\nLBJ\nMDY\nPTIN\nRTL\nSDOW\nSOXS\nSSG\nTECS\nTZA\nUVXY\nVIXY\nVPC\nXES\nXOP\nYANG\n31" + }, + { + "dataset": "stockmarket", + "query": "2", + "run": "1", + "answer": "BOIL\nBZQ\nCOM\nDUST\nEDZ\nERX\nFAZ\nFIN\nFP\nGUSH\nHYUP\nJDST\nJNUG\nJPN\nLABD\nLABU\nLBJ\nMDY\nPTIN\nRTL\nSDOW\nSOXS\nSSG\nTECS\nTZA\nUVXY\nVIXY\nVPC\nXES\nXOP\nYANG\nTotal: 31" + }, + { + "dataset": "stockmarket", + "query": "2", + "run": "2", + "answer": "BOIL\nBZQ\nCOM\nEDZ\nERX\nGFIN\nGUSH\nJDST\nJNUG\nJPN\nLABD\nLABU\nLBJ\nMDY\nPTIN\nRTL\nSDOW\nSOXS\nSSG\nTECS\nTZA\nUVXY\nXES\nXOP\nYANG\nTotal: 25" + }, + { + "dataset": "stockmarket", + "query": "2", + "run": "3", + "answer": "BOIL\nBZQ\nCOM\nDUST\nEDZ\nERX\nFAZ\nHYUP\nJDST\nJNUG\nJPN\nLABD\nLABU\nLBJ\nMDY\nPTIN\nRTL\nSDOW\nSOXS\nSSG\nTECS\nTZA\nUVXY\nVIXY\nVPC\nXES\nXOP\nYANG\nTotal: 28" + }, + { + "dataset": "stockmarket", + "query": "2", + "run": "4", + "answer": "BOIL\nBZQ\nCOM\nDUST\nEDZ\nERX\nFAZ\nFXP\nGFIN\nGUSH\nHYUP\nJDST\nJNUG\nJPN\nLABD\nLABU\nLBJ\nMDY\nPTIN\nRTL\nSOXS\nSSG\nTZA\nUVXY\nVIXY\nXOP\nYANG\nTotal: 27" + }, + { + "dataset": "stockmarket", + "query": "3", + "run": "0", + "answer": "APEX (Apex Global Brands Inc.): 23781.42\nBKYI (BIO-key International, Inc.): 10988.14\nCBAT (CBAK Energy Technology, Inc.): 86223.32\nCCCL (China Ceramics Co., Ltd.): 4366.80\nCORV (Correvio Pharma Corp.): 145247.83\nCPAH (CounterPath Corporation): 375.49\nDZSI (DASAN Zhone Solutions, Inc.): 15578.66\nFTFT (Future FinTech Group Inc.): 9.85\nFTR (Frontier Communications Corporation): 254397.63\nIDEX (Ideanomics, Inc.): 10.28\nOPTT (Ocean Power Technologies, Inc.): 254.15\nPEIX (Pacific Ethanol, Inc.): 10706.72\nSES (Synthesis Energy Systems, Inc.): 2390.51\nSNSS (Sunesis Pharmaceuticals, Inc.): 781.82\nSYPR (Sypris Solutions, Inc.): 36836.36" + }, + { + "dataset": "stockmarket", + "query": "3", + "run": "1", + "answer": "Apex Global Brands Inc., 23781.422924901184\nBIO-key International, Inc., 10988.142292490118\nCBAK Energy Technology, Inc., 86223.32015810277\nChina Ceramics Co., Ltd., 4366.798418972332\nCorrevio Pharma Corp., 145247.8260869565\nCounterPath Corporation, 375.49407114624506\nDASAN Zhone Solutions, Inc., 15578.656126482214\nFuture FinTech Group Inc., 9.845238095238095\nFrontier Communications Corporation, 254397.62845849802\nIdeanomics, Inc., 10.276679841897232\nOcean Power Technologies, Inc., 254.1501976284585\nPacific Ethanol, Inc., 10706.719367588932\nSynthesis Energy Systems, Inc., 2390.513833992095\nSunesis Pharmaceuticals, Inc., 781.8181818181819\nSypris Solutions, Inc., 36836.36363636364" + }, + { + "dataset": "stockmarket", + "query": "3", + "run": "2", + "answer": "APEX | Apex Global Brands Inc. | 23781.42\nBKYI | BIO-key International, Inc. | 10988.14\nCBAT | CBAK Energy Technology, Inc. | 86223.32\nCCCL | China Ceramics Co., Ltd. | 4366.80\nCORV | Correvio Pharma Corp. | 145247.83\nCPAH | CounterPath Corporation | 375.49\nDZSI | DASAN Zhone Solutions, Inc. | 15578.66\nFTFT | Future FinTech Group Inc. | 9.85\nFTR | Frontier Communications Corporation | 254397.63\nIDEX | Ideanomics, Inc. | 10.28\nOPTT | Ocean Power Technologies, Inc. | 254.15\nPEIX | Pacific Ethanol, Inc. | 10706.72\nSES | Synthesis Energy Systems, Inc. | 2390.51\nSNSS | Sunesis Pharmaceuticals, Inc. | 781.82\nSYPR | Sypris Solutions, Inc. | 36836.36" + }, + { + "dataset": "stockmarket", + "query": "3", + "run": "3", + "answer": "APEX (Apex Global Brands Inc.): 23781.42\nBKYI (BIO-key International, Inc.): 10988.14\nCBAT (CBAK Energy Technology, Inc.): 86223.32\nCCCL (China Ceramics Co., Ltd.): 4366.80\nCORV (Correvio Pharma Corp.): 145247.83\nCPAH (CounterPath Corporation): 375.49\nDZSI (DASAN Zhone Solutions, Inc.): 15578.66\nFTFT (Future FinTech Group Inc.): 9.85\nFTR (Frontier Communications Corporation): 254397.63\nIDEX (Ideanomics, Inc.): 10.28\nOPTT (Ocean Power Technologies, Inc.): 254.15\nPEIX (Pacific Ethanol, Inc.): 10706.72\nSES (Synthesis Energy Systems, Inc.): 2390.51\nSNSS (Sunesis Pharmaceuticals, Inc.): 781.82\nSYPR (Sypris Solutions, Inc.): 36836.36" + }, + { + "dataset": "stockmarket", + "query": "3", + "run": "4", + "answer": "Apex Global Brands Inc.: 23781.42\nBIO-key International, Inc.: 10988.14\nCBAK Energy Technology, Inc.: 86223.32\nChina Ceramics Co., Ltd.: 4366.80\nCorrevio Pharma Corp.: 145247.83\nCounterPath Corporation: 375.49\nDASAN Zhone Solutions, Inc.: 15578.66\nFrontier Communications Corporation: 254397.63\nFuture FinTech Group Inc.: 9.85\nIdeanomics, Inc.: 10.28\nOcean Power Technologies, Inc.: 254.15\nPacific Ethanol, Inc.: 10706.72\nSunesis Pharmaceuticals, Inc.: 781.82\nSynthesis Energy Systems, Inc.: 2390.51\nSypris Solutions, Inc.: 36836.36" + }, + { + "dataset": "stockmarket", + "query": "4", + "run": "0", + "answer": "MFA Financial, Inc.\nArgo Group International Holdings, Ltd.\nHDFC Bank Limited\nAlbany International Corporation\nDTE Energy Company" + }, + { + "dataset": "stockmarket", + "query": "4", + "run": "1", + "answer": "MFA Financial, Inc.\nArgo Group International Holdings, Ltd.\nHDFC Bank Limited\nAlbany International Corporation\nDTE Energy Company" + }, + { + "dataset": "stockmarket", + "query": "4", + "run": "2", + "answer": "MFA Financial, Inc.\nArgo Group International Holdings, Ltd.\nHDFC Bank Limited\nAlbany International Corporation\nDTE Energy Company" + }, + { + "dataset": "stockmarket", + "query": "4", + "run": "3", + "answer": "MFA Financial, Inc.\nHDFC Bank Limited\nAlbany International Corporation\nDTE Energy Company\nGetty Realty Corporation" + }, + { + "dataset": "stockmarket", + "query": "4", + "run": "4", + "answer": "MFA Financial, Inc.\nArgo Group International Holdings, Ltd.\nHDFC Bank Limited\nAlbany International Corporation\nDTE Energy Company" + }, + { + "dataset": "stockmarket", + "query": "5", + "run": "0", + "answer": "Synthesis Energy Systems, Inc.\nTD Holdings, Inc.\nTMSR Holding Company Limited\nVerb Technology Company, Inc.\nSunesis Pharmaceuticals, Inc." + }, + { + "dataset": "stockmarket", + "query": "5", + "run": "1", + "answer": "Synthesis Energy Systems, Inc.\nTD Holdings, Inc.\nTMSR Holding Company Limited\nVerb Technology Company, Inc.\nSunesis Pharmaceuticals, Inc." + }, + { + "dataset": "stockmarket", + "query": "5", + "run": "2", + "answer": "Synthesis Energy Systems, Inc.\nTD Holdings, Inc.\nTMSR Holding Company Limited\nVerb Technology Company, Inc.\nSunesis Pharmaceuticals, Inc." + }, + { + "dataset": "stockmarket", + "query": "5", + "run": "3", + "answer": "Synthesis Energy Systems, Inc.\nTD Holdings, Inc.\nTMSR Holding Company Limited\nVerb Technology Company, Inc.\nSunesis Pharmaceuticals, Inc." + }, + { + "dataset": "stockmarket", + "query": "5", + "run": "4", + "answer": "Synthesis Energy Systems, Inc.\nTD Holdings, Inc.\nTMSR Holding Company Limited\nVerb Technology Company, Inc.\nSunesis Pharmaceuticals, Inc." + }, + { + "dataset": "yelp", + "query": "1", + "run": "0", + "answer": "3.547" + }, + { + "dataset": "yelp", + "query": "1", + "run": "1", + "answer": "3.547" + }, + { + "dataset": "yelp", + "query": "1", + "run": "2", + "answer": "3.547008547008547" + }, + { + "dataset": "yelp", + "query": "1", + "run": "3", + "answer": "3.55" + }, + { + "dataset": "yelp", + "query": "1", + "run": "4", + "answer": "3.547" + }, + { + "dataset": "yelp", + "query": "2", + "run": "0", + "answer": "PA (Pennsylvania) has the highest number of reviews with 662 reviews, and the average rating of businesses in that state is 3.6994 (approximately 3.70)." + }, + { + "dataset": "yelp", + "query": "2", + "run": "1", + "answer": "PA\n3.6994" + }, + { + "dataset": "yelp", + "query": "2", + "run": "2", + "answer": "Let me add an alias to the MongoDB business_ref field so it appears in the output, and swap to DuckDB being leftmost." + }, + { + "dataset": "yelp", + "query": "2", + "run": "3", + "answer": "PA, 3.6994" + }, + { + "dataset": "yelp", + "query": "2", + "run": "4", + "answer": "PA\n3.70" + }, + { + "dataset": "yelp", + "query": "3", + "run": "0", + "answer": "35" + }, + { + "dataset": "yelp", + "query": "3", + "run": "1", + "answer": "35" + }, + { + "dataset": "yelp", + "query": "3", + "run": "2", + "answer": "35" + }, + { + "dataset": "yelp", + "query": "3", + "run": "3", + "answer": "35" + }, + { + "dataset": "yelp", + "query": "3", + "run": "4", + "answer": "35" + }, + { + "dataset": "yelp", + "query": "4", + "run": "0", + "answer": "Restaurants\n3.6174" + }, + { + "dataset": "yelp", + "query": "4", + "run": "1", + "answer": "I found the issue \u2014 the left-side (first sub-query) columns are un-prefixed, so the column should be `description`, not `mg__description`. That's why `text_extract_list` returned null. Let me fix the column name and run the full pipeline." + }, + { + "dataset": "yelp", + "query": "4", + "run": "2", + "answer": "Restaurants\n3.6174" + }, + { + "dataset": "yelp", + "query": "4", + "run": "3", + "answer": "Restaurants, 3.6174" + }, + { + "dataset": "yelp", + "query": "4", + "run": "4", + "answer": "Restaurants, 3.64" + }, + { + "dataset": "yelp", + "query": "5", + "run": "0", + "answer": "Pennsylvania (PA) \u2014 8 businesses offering WiFi, with an average rating of 3.48" + }, + { + "dataset": "yelp", + "query": "5", + "run": "1", + "answer": "PA, 3.48" + }, + { + "dataset": "yelp", + "query": "5", + "run": "2", + "answer": "Pennsylvania\n3.48" + }, + { + "dataset": "yelp", + "query": "5", + "run": "3", + "answer": "PA (Pennsylvania) has the highest number of businesses that offer WiFi with 8 businesses, and the average rating for those businesses is 3.48." + }, + { + "dataset": "yelp", + "query": "5", + "run": "4", + "answer": "Pennsylvania (PA) has the highest number of WiFi-offering businesses with 8, and their average rating is 3.48." + }, + { + "dataset": "yelp", + "query": "6", + "run": "0", + "answer": "Coffee House Too Cafe\nRestaurants, Breakfast & Brunch, American (New), Cafes" + }, + { + "dataset": "yelp", + "query": "6", + "run": "1", + "answer": "Coffee House Too Cafe\nRestaurants, Breakfast & Brunch, American (New), Cafes" + }, + { + "dataset": "yelp", + "query": "6", + "run": "2", + "answer": "Coffee House Too Cafe\nCategories: Restaurants, Breakfast & Brunch, American (New), Cafes\nAverage rating: 4.375" + }, + { + "dataset": "yelp", + "query": "6", + "run": "3", + "answer": "Coffee House Too Cafe\nRestaurants, Breakfast & Brunch, American (New), Cafes" + }, + { + "dataset": "yelp", + "query": "6", + "run": "4", + "answer": "Coffee House Too Cafe\nRestaurants, Breakfast & Brunch, American (New), Cafes" + }, + { + "dataset": "yelp", + "query": "7", + "run": "0", + "answer": "Restaurants\nFood\nAmerican (New)\nShopping\nBreakfast & Brunch" + }, + { + "dataset": "yelp", + "query": "7", + "run": "1", + "answer": "Restaurants\nFood\nAmerican (New)\nShopping\nBreakfast & Brunch" + }, + { + "dataset": "yelp", + "query": "7", + "run": "2", + "answer": "Food\nRestaurants\nShopping\nAmerican (New)\nBreakfast & Brunch" + }, + { + "dataset": "yelp", + "query": "7", + "run": "3", + "answer": "As expected, the join returned 0 rows because the values use different prefixes (`businessref_` vs `businessid_`). I need to extract the numeric ID from both sides and join on that. Let me fix the join keys." + }, + { + "dataset": "yelp", + "query": "7", + "run": "4", + "answer": "Restaurants\nFood\nAmerican (New)\nShopping\nBreakfast & Brunch" + } +] \ No newline at end of file