MMTU-benchmark/MMTU huggingface dataset:
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                       | authors                             | venue         |   year |
|:----------------------------------------------------------------------------|:------------------------------------|:--------------|-------:|
| extraction of object-oriented structures from existing relational databases | julia e. hodges , shekar ramanathan | sigmod record |   1997 |

Entity B:
| title                                                                                      | authors                                               | venue                 |   year |
|:-------------------------------------------------------------------------------------------|:------------------------------------------------------|:----------------------|-------:|
| cost-based selection of path expression processing algorithms in object-oriented databases | georges gardarin , jean-robert gruser , zhao-hui tang | very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1542
rtable_id: 1125
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                  | authors            | venue   |   year |
|:-------------------------------------------------------|:-------------------|:--------|-------:|
| searching and mining fine-grained semi-structured data | soumen chakrabarti | vldb    |   2002 |

Entity B:
| title                          | authors       | venue             |   year |
|:-------------------------------|:--------------|:------------------|-------:|
| describing semistructured data | luca cardelli | acm sigmod record |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1159
rtable_id: 594
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                  | authors                                                    | venue                       |   year |
|:---------------------------------------------------------------------------------------|:-----------------------------------------------------------|:----------------------------|-------:|
| description logics for semantic query optimization in object-oriented database systems | claudio sartori , sonia bergamaschi , domenico beneventano | acm trans . database syst . |   2003 |

Entity B:
| title                                                                         | authors                  | venue             |   year |
|:------------------------------------------------------------------------------|:-------------------------|:------------------|-------:|
| cost estimation of user-defined methods in object-relational database systems | jihad boulos , kinji ono | acm sigmod record |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2008
rtable_id: 87
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                    | authors                                                                             | venue   |   year |
|:---------------------------------------------------------|:------------------------------------------------------------------------------------|:--------|-------:|
| a data transformation system for biological data sources | susan b. davidson , limsoon wong , kyle hart , peter buneman , g. christian overton | vldb    |   1995 |

Entity B:
| title                                          | authors                                        | venue                                          |   year |
|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|-------:|
| a system for watermarking relational databases | rakesh agrawal , peter j. haas , jerry kiernan | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 567
rtable_id: 2019
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                         | authors                                           | venue   |   year |
|:----------------------------------------------|:--------------------------------------------------|:--------|-------:|
| database technologies for electronic commerce | yirong xu , ramakrishnan srikant , rakesh agrawal | vldb    |   2002 |

Entity B:
| title                                         | authors      | venue             |   year |
|:----------------------------------------------|:-------------|:------------------|-------:|
| data management issues in electronic commerce | asuman dogac | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1074
rtable_id: 347
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                  | authors                                                  | venue   |   year |
|:-----------------------------------------------------------------------|:---------------------------------------------------------|:--------|-------:|
| an efficient algorithm for mining association rules in large databases | edward omiecinski , shamkant b. navathe , ashok savasere | vldb    |   1995 |

Entity B:
| title                                                                                      | authors                                                                    | venue                                          |   year |
|:-------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| cmvf : a novel dimension reduction scheme for efficient indexing in a large image database | jialie shen , anne h. h. ngu , john shepherd , du q. huynh , quan z. sheng | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 863
rtable_id: 2003
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                    | authors                                                            | venue             |   year |
|:-------------------------------------------------------------------------|:-------------------------------------------------------------------|:------------------|-------:|
| simultaneous optimization and evaluation of multiple dimensional queries | amit shukla , jeffrey f. naughton , yihong zhao , prasad deshpande | sigmod conference |   1998 |

Entity B:
| title                                                 | authors                                     | venue                                          |   year |
|:------------------------------------------------------|:--------------------------------------------|:-----------------------------------------------|-------:|
| orthogonal optimization of subqueries and aggregation | c &#233; sar galindo-legaria , milind joshi | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1220
rtable_id: 279
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                     | authors                                                      | venue   |   year |
|:------------------------------------------|:-------------------------------------------------------------|:--------|-------:|
| dynamic update cube for range-sum queries | seok-ju chun , ju-hong lee , seok-lyong lee , chin-wan chung | vldb    |   2001 |

Entity B:
| title                                     | authors                                                      | venue                 |   year |
|:------------------------------------------|:-------------------------------------------------------------|:----------------------|-------:|
| dynamic update cube for range-sum queries | seok-ju chun , chin-wan chung , ju-hong lee , seok-lyong lee | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2604
rtable_id: 1334
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                           | authors                       | venue         |   year |
|:--------------------------------|:------------------------------|:--------------|-------:|
| sql/xml is making good progress | andrew eisenberg , jim melton | sigmod record |   2002 |

Entity B:
| title                           | authors                       | venue             |   year |
|:--------------------------------|:------------------------------|:------------------|-------:|
| sql/xml is making good progress | andrew eisenberg , jim melton | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1681
rtable_id: 531
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                              | authors                                                | venue             |   year |
|:---------------------------------------------------|:-------------------------------------------------------|:------------------|-------:|
| data integration and warehousing in telecom italia | daniele nardi , maurizio lenzerini , stefano trisolini | sigmod conference |   1999 |

Entity B:
| title                                          | authors                            | venue                                          |   year |
|:-----------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| data warehousing and olap for decision support | surajit chaudhuri , umeshwar dayal | international conference on management of data |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1237
rtable_id: 1848
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                     | authors                                               | venue             |   year |
|:----------------------------------------------------------|:------------------------------------------------------|:------------------|-------:|
| processing set expressions over continuous update streams | sumit ganguly , minos n. garofalakis , rajeev rastogi | sigmod conference |   2003 |

Entity B:
| title                                                 | authors                                                                 | venue                                          |   year |
|:------------------------------------------------------|:------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| continuously adaptive continuous queries over streams | samuel madden , mehul shah , joseph m. hellerstein , vijayshankar raman | international conference on management of data |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 105
rtable_id: 647
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                 | authors                             | venue   |   year |
|:--------------------------------------|:------------------------------------|:--------|-------:|
| data compression support in databases | david wilhite , balakrishna r. iyer | vldb    |   1994 |

Entity B:
| title                                             | authors                                    | venue                                          |   year |
|:--------------------------------------------------|:-------------------------------------------|:-----------------------------------------------|-------:|
| query optimization in compressed database systems | zhiyuan chen , johannes gehrke , flip korn | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 349
rtable_id: 304
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title           | authors                            | venue             |   year |
|:----------------|:-----------------------------------|:------------------|-------:|
| the naos system | christine collet , thierry coupaye | sigmod conference |   1995 |

Entity B:
| title                          | authors                                | venue                                          |   year |
|:-------------------------------|:---------------------------------------|:-----------------------------------------------|-------:|
| the ecrc multi database system | willem jonker , heribert sch &#252; tz | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1082
rtable_id: 1939
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                           | authors                                | venue   |   year |
|:------------------------------------------------|:---------------------------------------|:--------|-------:|
| query processing and optimization in oracle rdb | gennady antoshenkov , mohamed ziauddin | vldb j. |   1996 |

Entity B:
| title                                           | authors       | venue                                          |   year |
|:------------------------------------------------|:--------------|:-----------------------------------------------|-------:|
| query optimization for selections using bitmaps | ming-chuan wu | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1169
rtable_id: 207
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                | authors                                         | venue   |   year |
|:-------------------------------------------------------------------------------------|:------------------------------------------------|:--------|-------:|
| optimizing multiple dimensional queries simultaneously in multidimensional databases | jeffrey xu yu , weifa liang , maria e. orlowska | vldb j. |   2000 |

Entity B:
| title                       | authors                        | venue                                          |   year |
|:----------------------------|:-------------------------------|:-----------------------------------------------|-------:|
| optimizing queries on files | mariano p. consens , tova milo | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2062
rtable_id: 1637
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                          | authors                                                                                                                 | venue   |   year |
|:-----------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:--------|-------:|
| anatomy of a native xml base management system | thorsten fiebig , till westmann , robert schiele , guido moerkotte , carl-christian kanne , julia neumann , sven helmer | vldb j. |   2002 |

Entity B:
| title                                             | authors                                                   | venue             |   year |
|:--------------------------------------------------|:----------------------------------------------------------|:------------------|-------:|
| a query language for a web-site management system | mary fernandez , daniela florescu , alon levy , dan suciu | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 508
rtable_id: 1862
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title              | authors     | venue         |   year |
|:-------------------|:------------|:--------------|-------:|
| book review column | karl aberer | sigmod record |   2002 |

Entity B:
| title        | authors     | venue             |   year |
|:-------------|:------------|:------------------|-------:|
| book reviews | karl aberer | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1899
rtable_id: 2124
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                            | authors       | venue   |   year |
|:-------------------------------------------------|:--------------|:--------|-------:|
| index configuration in object-oriented databases | elisa bertino | vldb j. |   1994 |

Entity B:
| title                                                                                      | authors                                               | venue                 |   year |
|:-------------------------------------------------------------------------------------------|:------------------------------------------------------|:----------------------|-------:|
| cost-based selection of path expression processing algorithms in object-oriented databases | georges gardarin , jean-robert gruser , zhao-hui tang | very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 644
rtable_id: 1125
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                     | authors                       | venue   |   year |
|:------------------------------------------------------------------------------------------|:------------------------------|:--------|-------:|
| a performance study of four index structures for set-valued attributes of low cardinality | guido moerkotte , sven helmer | vldb j. |   2003 |

Entity B:
| title                                                                                     | authors                       | venue                                                                  |   year |
|:------------------------------------------------------------------------------------------|:------------------------------|:-----------------------------------------------------------------------|-------:|
| a performance study of four index structures for set-valued attributes of low cardinality | sven helmer , guido moerkotte | the vldb journal -- the international journal on very large data bases |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1193
rtable_id: 2270
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                                     | authors                                                                          | venue         |   year |
|:----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------|-------:|
| electronic market : the roadmap for university libraries and members to survive in the information jungle | peter c. lockemann , bethina schmitt , michael christoffel , sebastian pulkowski | sigmod record |   1998 |

Entity B:
| title                                                                                                     | authors                                                                          | venue             |   year |
|:----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------|-------:|
| electronic market : the roadmap for university libraries and members to survive in the information jungle | michael christoffel , sebastian pulkowski , bethina schmitt , peter c. lockemann | acm sigmod record |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 672
rtable_id: 114
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                          | authors        | venue   |   year |
|:-----------------------------------------------|:---------------|:--------|-------:|
| sampling large databases for association rules | hannu toivonen | vldb    |   1996 |

Entity B:
| title                                                                              | authors                                                                | venue                                          |   year |
|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------------------------------------|-------:|
| locally adaptive dimensionality reduction for indexing large time series databases | eamonn keogh , kaushik chakrabarti , michael pazzani , sharad mehrotra | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1428
rtable_id: 303
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                               | authors                                                      | venue         |   year |
|:------------------------------------|:-------------------------------------------------------------|:--------------|-------:|
| reminiscences an influential papers | jeffrey f. naughton , minos n. garofalakis , kenneth a. ross | sigmod record |   2003 |

Entity B:
| title                               | authors         | venue             |   year |
|:------------------------------------|:----------------|:------------------|-------:|
| reminiscences on influential papers | kenneth a. ross | acm sigmod record |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 131
rtable_id: 564
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                        | authors                              | venue   |   year |
|:---------------------------------------------|:-------------------------------------|:--------|-------:|
| a foundation for multi-dimensional databases | laks v. s. lakshmanan , marc gyssens | vldb    |   1997 |

Entity B:
| title                             | authors       | venue                                          |   year |
|:----------------------------------|:--------------|:-----------------------------------------------|-------:|
| standard for multimedia databases | john r. smith | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2595
rtable_id: 368
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                  | authors                                 | venue   |   year |
|:-------------------------------------------------------|:----------------------------------------|:--------|-------:|
| introduction ( special issue on multimedia databases ) | m. tamer özsu , stavros christodoulakis | vldb j. |   1998 |

Entity B:
| title                                     | authors     | venue                                                                  |   year |
|:------------------------------------------|:------------|:-----------------------------------------------------------------------|-------:|
| special issue on spatial database systems | h. j. schek | the vldb journal -- the international journal on very large data bases |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1091
rtable_id: 588
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                     | authors                                                                     | venue                       |   year |
|:------------------------------------------|:----------------------------------------------------------------------------|:----------------------------|-------:|
| space optimization in deductive databases | s. sudarshan , divesh srivastava , raghu ramakrishnan , jeffrey f. naughton | acm trans . database syst . |   1995 |

Entity B:
| title                                                                 | authors                              | venue                 |   year |
|:----------------------------------------------------------------------|:-------------------------------------|:----------------------|-------:|
| heterogeneous database query optimization in db2 universal datajoiner | shivakumar venkataraman , tian zhang | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 94
rtable_id: 971
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                           | authors   | venue             |   year |
|:----------------------------------------------------------------|:----------|:------------------|-------:|
| unisql/x unified relational and object-oriented database system | won kim   | sigmod conference |   1994 |

Entity B:
| title                                                                         | authors                  | venue             |   year |
|:------------------------------------------------------------------------------|:-------------------------|:------------------|-------:|
| cost estimation of user-defined methods in object-relational database systems | jihad boulos , kinji ono | acm sigmod record |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2497
rtable_id: 87
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                               | authors                                     | venue             |   year |
|:----------------------------------------------------|:--------------------------------------------|:------------------|-------:|
| scalable parallel data mining for association rules | eui-hong han , vipin kumar , george karypis | sigmod conference |   1997 |

Entity B:
| title                                                           | authors                    | venue                                          |   year |
|:----------------------------------------------------------------|:---------------------------|:-----------------------------------------------|-------:|
| communication-efficient distributed mining of association rules | assaf schuster , ran wolff | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 146
rtable_id: 258
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                               | authors                                                                                                      | venue             |   year |
|:--------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| storing and querying ordered xml using a relational database system | kevin s. beyer , jayavel shanmugasundaram , chun zhang , igor tatarinov , stratis viglas , eugene j. shekita | sigmod conference |   2002 |

Entity B:
| title                                          | authors                                        | venue                                          |   year |
|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|-------:|
| a system for watermarking relational databases | rakesh agrawal , peter j. haas , jerry kiernan | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1098
rtable_id: 2019
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                              | authors                       | venue             |   year |
|:-----------------------------------|:------------------------------|:------------------|-------:|
| on-line warehouse view maintenance | jennifer widom , dallan quass | sigmod conference |   1997 |

Entity B:
| title                                         | authors                                                                          | venue                                          |   year |
|:----------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| view maintenance in a warehousing environment | yue zhuge , h &#233; ctor garc &#237; a-molina , joachim hammer , jennifer widom | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1142
rtable_id: 1507
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                       | authors                                                   | venue             |   year |
|:--------------------------------------------|:----------------------------------------------------------|:------------------|-------:|
| fast-start : quick fault recovery in oracle | ashok joshi , ron weiss , amit ganesh , tirthankar lahiri | sigmod conference |   2001 |

Entity B:
| title                                       | authors                                                   | venue                                          |   year |
|:--------------------------------------------|:----------------------------------------------------------|:-----------------------------------------------|-------:|
| fast-start : quick fault recovery in oracle | tirthankar lahiri , amit ganesh , ron weiss , ashok joshi | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2212
rtable_id: 277
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                    | authors                                                                                                                                                                                                                                                   | venue             |   year |
|:-----------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| aurora : a data stream management system | nesime tatbul , daniel j. abadi , c. erwin , anurag maskey , mitch cherniack , alex rasin , christian convey , a. singer , eduardo f. galvez , r. yan , ugur çetintemel , ying xing , stanley b. zdonik , michael stonebraker , donald carney , m. hatoun | sigmod conference |   2003 |

Entity B:
| title                                    | authors                  | venue             |   year |
|:-----------------------------------------|:-------------------------|:------------------|-------:|
| data management for earth system science | james frew , jeff dozier | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 11
rtable_id: 1918
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title     | authors              | venue                       |   year |
|:----------|:---------------------|:----------------------------|-------:|
| editorial | richard t. snodgrass | acm trans . database syst . |   2001 |

Entity B:
| title           | authors        | venue                                                                  |   year |
|:----------------|:---------------|:-----------------------------------------------------------------------|-------:|
| guest editorial | matthias jarke | the vldb journal -- the international journal on very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2371
rtable_id: 1178
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                        | authors                                                                                      | venue   |   year |
|:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:--------|-------:|
| an effective deductive object-oriented database through language integration | m. howard williams , norman w. paton , andrew dinn , maria l. barja , alvaro a. a. fernandes | vldb    |   1994 |

Entity B:
| title                                                           | authors       | venue                                          |   year |
|:----------------------------------------------------------------|:--------------|:-----------------------------------------------|-------:|
| static detection of security flaws in object-oriented databases | keishi tajima | international conference on management of data |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 783
rtable_id: 1983
label: 0