prompt stringlengths 665 627k | metadata stringlengths 286 56.7k | task stringclasses 25
values | dataset stringclasses 46
values |
|---|---|---|---|
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "CountryCodeToCountry", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/CountryCodeToCountry", "case": "CountryCodeToCountry", "label": [["93", "... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "RGBToColor", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/RGBToColor", "case": "RGBToColor", "label": [["84;84;84", "Grey"], ["211;211;211", ... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "NBAClub2Roster", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/NBAClub2Roster", "case": "NBAClub2Roster", "label": [["Golden State Warriors", ... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "PatentIDToName", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/PatentIDToName", "case": "PatentIDToName", "label": [["D321987", "Handleless To... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "UnescoSite2Location", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/UnescoSite2Location", "case": "UnescoSite2Location", "label": [["Old Havan... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "CountryToAreaSqKM", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/CountryToAreaSqKM", "case": "CountryToAreaSqKM", "label": [["Afghanistan", 6... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "TeamToManager", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/TeamToManager", "case": "TeamToManager", "label": [["Golden State Warriors", "Bo... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "Worldcupwinner2year", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/Worldcupwinner2year", "case": "Worldcupwinner2year", "label": [["Germany",... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "SoccerPlayer2club", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/SoccerPlayer2club", "case": "SoccerPlayer2club", "label": [["Messi", "FC Bar... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "CountryToTwoLettersISOCode", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/CountryToTwoLettersISOCode", "case": "CountryToTwoLettersISOCode", ... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "IBANToBankName", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/IBANToBankName", "case": "IBANToBankName", "label": [["DE49 5185 0079 0002 0001... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "AwardToArtist", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/AwardToArtist", "case": "AwardToArtist", "label": [["2x-Platin", "Abba"], ["Gold... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "RegionToAreaCode-Single", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/RegionToAreaCode-Single", "case": "RegionToAreaCode-Single", "label": ... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "MountainsOver7k2Range", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/MountainsOver7k2Range", "case": "MountainsOver7k2Range", "label": [["Mou... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "StateToAbbrv", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/StateToAbbrv", "case": "StateToAbbrv", "label": [["Alabama", "AL"], ["Alaska", "A... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "Country2UnescoSites", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/Country2UnescoSites", "case": "Country2UnescoSites", "label": [["Cuba", "O... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "SoccerPlayer2Birthplace", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/SoccerPlayer2Birthplace", "case": "SoccerPlayer2Birthplace", "label": ... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "CountryToCurrencies", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/CountryToCurrencies", "case": "CountryToCurrencies", "label": [["Afghanist... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "Movie2Cast", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/Movie2Cast", "case": "Movie2Cast", "label": [["Platoon", "Tom Berenger"], ["Platoon... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "ManufacturerToProducts", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/ManufacturerToProducts", "case": "ManufacturerToProducts", "label": [["... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "AirportcodeToCity", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/AirportcodeToCity", "case": "AirportcodeToCity", "label": [["het", "hohhot"]... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "CompanyToTicker", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/CompanyToTicker", "case": "CompanyToTicker", "label": [["The New York Times Co... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "ZipToCity", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/ZipToCity", "case": "ZipToCity", "label": [[90638, "LA MIRADA"], [10997, "West Point... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "SwiftToBankName", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/SwiftToBankName", "case": "SwiftToBankName", "label": [["ASCBVNVX", "ASIA COMM... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "BankToCity", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/BankToCity", "case": "BankToCity", "label": [["COMMERCIAL BANK -LANTA-BANK'", "MOSC... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "BankToSwiftCode", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/BankToSwiftCode", "case": "BankToSwiftCode", "label": [["ALWATANY BANK OF EGYP... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "CityToZIp", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/CityToZIp", "case": "CityToZIp", "label": [["LA MIRADA", 90638], ["West Point", 1099... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "bankToCountryDistinct", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/bankToCountryDistinct", "case": "bankToCountryDistinct", "label": [["BAN... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "YearNobelPrizeFieldLaureate", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/YearNobelPrizeFieldLaureate", "case": "YearNobelPrizeFieldLaureate... | semantic-join | DataXFormer |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer... | {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "DataXFormer", "test_case": "YearNobelPrizeLaureate", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/DataXFormer/YearNobelPrizeLaureate", "case": "YearNobelPrizeLaureate", "label": [[1... | semantic-join | DataXFormer |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_8527", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_8527", "label": [], "labeled_data": [[["Foreign_72", "Foreign_15"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3338", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3338", "label": [], "labeled_data": [[["Foreign_54"], "Foreign_17"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4625", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4625", "label": [], "labeled_data": [[["Foreign_13", "Foreign_46"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3906", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3906", "label": [], "labeled_data": [[["Foreign_14", "Foreign_4"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_12529", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_12529", "label": [], "labeled_data": [[["Foreign_86", "Foreign_38"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_422", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_422", "label": [], "labeled_data": [[["Foreign_90", "Foreign_31", "F... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4111", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4111", "label": [], "labeled_data": [[["Foreign_40", "Foreign_1"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9208", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9208", "label": [], "labeled_data": [[["Foreign_2"], "Foreign_23"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_12514", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_12514", "label": [], "labeled_data": [[["Foreign_15", "Foreign_3",... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_502", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_502", "label": [], "labeled_data": [[["Foreign_46", "Foreign_53", "F... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1722", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1722", "label": [], "labeled_data": [[["Foreign_3", "Foreign_70"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_2339", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_2339", "label": [], "labeled_data": [[["Foreign_74", "Foreign_29"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_10805", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_10805", "label": [], "labeled_data": [[["Foreign_23", "Foreign_25"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_12444", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_12444", "label": [], "labeled_data": [[["Foreign_69", "Foreign_26"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3673", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3673", "label": [], "labeled_data": [[["Foreign_41", "Foreign_12", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_7107", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_7107", "label": [], "labeled_data": [[["Foreign_88", "Foreign_45"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4676", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4676", "label": [], "labeled_data": [[["Foreign_25", "Foreign_55", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_14693", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_14693", "label": [], "labeled_data": [[["Foreign_77", "Foreign_98"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_111", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_111", "label": [], "labeled_data": [[["Foreign_16", "Foreign_26", "F... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1873", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1873", "label": [], "labeled_data": [[["Foreign_88"], "Foreign_23"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_5721", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_5721", "label": [], "labeled_data": [[["Foreign_0"], "Foreign_48"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9201", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9201", "label": [], "labeled_data": [[["Foreign_35", "Foreign_92", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_7119", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_7119", "label": [], "labeled_data": [[["Foreign_35"], "Foreign_59"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3617", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3617", "label": [], "labeled_data": [[["Foreign_72", "Foreign_74"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9961", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9961", "label": [], "labeled_data": [[["Foreign_9", "Foreign_11", "... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1463", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1463", "label": [], "labeled_data": [[["Foreign_22", "Foreign_0"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3702", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3702", "label": [], "labeled_data": [[["Foreign_8", "Foreign_61", "... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_13610", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_13610", "label": [], "labeled_data": [[["Foreign_20", "Foreign_39"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9478", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9478", "label": [], "labeled_data": [[["Foreign_71", "Foreign_38", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_6395", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_6395", "label": [], "labeled_data": [[["Foreign_47"], "Foreign_17"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1627", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1627", "label": [], "labeled_data": [[["Foreign_20"], "Foreign_48"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_735", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_735", "label": [], "labeled_data": [[["Foreign_43", "Foreign_44"], "... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_2678", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_2678", "label": [], "labeled_data": [[["Foreign_6", "Foreign_26"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1562", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1562", "label": [], "labeled_data": [[["Foreign_37", "Foreign_71"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9982", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9982", "label": [], "labeled_data": [[["Foreign_63", "Foreign_95"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_12330", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_12330", "label": [], "labeled_data": [[["Foreign_22", "DK"], "Fore... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_7748", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_7748", "label": [], "labeled_data": [[["Foreign_40", "Foreign_0"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_5661", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_5661", "label": [], "labeled_data": [[["Foreign_32", "Foreign_96", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_10981", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_10981", "label": [], "labeled_data": [[["Foreign_57"], "Foreign_3"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_11856", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_11856", "label": [], "labeled_data": [[["Foreign_5", "Foreign_15"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_448", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_448", "label": [], "labeled_data": [[["Foreign_19", "Foreign_28", "F... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4448", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4448", "label": [], "labeled_data": [[["Foreign_21", "Foreign_78"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_11439", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_11439", "label": [], "labeled_data": [[["Foreign_6", "Foreign_2", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_6371", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_6371", "label": [], "labeled_data": [[["Foreign_4", "Foreign_67"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_16168", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_16168", "label": [], "labeled_data": [[["Foreign_9", "Foreign_0", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_15580", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_15580", "label": [], "labeled_data": [[["Foreign_8", "Foreign_88"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9324", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9324", "label": [], "labeled_data": [[["Foreign_18"], "Foreign_1"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_14565", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_14565", "label": [], "labeled_data": [[["Foreign_8", "Foreign_15"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_14319", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_14319", "label": [], "labeled_data": [[["Foreign_16", "Foreign_30"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_10620", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_10620", "label": [], "labeled_data": [[["Foreign_31"], "Foreign_26... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_14007", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_14007", "label": [], "labeled_data": [[["Foreign_26", "Foreign_7"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_6083", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_6083", "label": [], "labeled_data": [[["Foreign_11", "Foreign_48"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1173", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1173", "label": [], "labeled_data": [[["Foreign_29", "Foreign_36", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9764", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9764", "label": [], "labeled_data": [[["Foreign_75", "Foreign_48"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4671", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4671", "label": [], "labeled_data": [[["Foreign_2", "Foreign_27"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_10448", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_10448", "label": [], "labeled_data": [[["Foreign_26", "Foreign_14"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_11803", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_11803", "label": [], "labeled_data": [[["Foreign_22"], "Foreign_47... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_11910", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_11910", "label": [], "labeled_data": [[["Foreign_7"], "Foreign_31"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1578", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1578", "label": [], "labeled_data": [[["Foreign_8"], "Foreign_31"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3749", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3749", "label": [], "labeled_data": [[["Foreign_4", "Foreign_25", "... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4865", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4865", "label": [], "labeled_data": [[["Foreign_43", "Foreign_0"], ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3228", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3228", "label": [], "labeled_data": [[["Foreign_42"], "Foreign_62"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_1346", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_1346", "label": [], "labeled_data": [[["Foreign_63"], "Foreign_39"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_13633", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_13633", "label": [], "labeled_data": [[["Foreign_35", "Foreign_11"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_9052", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_9052", "label": [], "labeled_data": [[["Foreign_43", "Foreign_13"],... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_5784", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_5784", "label": [], "labeled_data": [[["Foreign_34", "Foreign_14", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_3827", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_3827", "label": [], "labeled_data": [[["Foreign_33", "Foreign_15", ... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_4673", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_4673", "label": [], "labeled_data": [[["Foreign_91"], "Foreign_41"]... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_10740", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_10740", "label": [], "labeled_data": [[["Foreign_11", "Foreign_14"... | String-Relationship | Auto-Relate |
You are given a table below, where some of the columns may be derived from other columns using string-based transformations (e.g., string split, concatenation, formatting, substring extraction, and pattern matching, etc.).
Your task is to identify string transformation relationships between the columns, by determinin... | {"task": "String-Relationship", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "Auto-Relate", "test_case": "case_14967", "case_path": "$MMTU_HOME/data/String-Relationship/sample1000-3shots/Auto-Relate/case_14967", "label": [], "labeled_data": [[["Foreign_21", "Foreign_2"]... | String-Relationship | Auto-Relate |
Subsets and Splits
Cisco Random Formula Prediction Case 166
Retrieves a specific subset of training data with particular task, dataset, and metadata conditions, but only returns raw examples without providing broader analytical insights.