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 |
|:------------------------------------|:-------------------------------------------------------------|:--------------|-------:|
| 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 |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 131
rtable_id: 927
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 |
|:----------------|:---------------|:--------|-------:|
| guest editorial | alon y. halevy | vldb j. |   2002 |

Entity B:
| title           | authors                                    | venue                                                                  |   year |
|:----------------|:-------------------------------------------|:-----------------------------------------------------------------------|-------:|
| guest editorial | vijay atluri , anupam joshi , yelena yesha | the vldb journal -- the international journal on very large data bases |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 471
rtable_id: 2184
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 |
|:-----------------------------------------------|:---------------------------------------------|:------------------|-------:|
| meaningful change detection in structured data | hector garcia-molina , sudarshan s. chawathe | sigmod conference |   1997 |

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: 1931
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 |
|:----------------------------------------------------|:-----------------------------------------------------------------|:--------|-------:|
| integration of data mining with database technology | amir netz , surajit chaudhuri , jeff bernhardt , usama m. fayyad | vldb    |   2000 |

Entity B:
| title                                               | authors                            | venue             |   year |
|:----------------------------------------------------|:-----------------------------------|:------------------|-------:|
| an overview of data warehousing and olap technology | surajit chaudhuri , umeshwar dayal | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1971
rtable_id: 1913
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 |
|:----------------------------------------------------------------|:--------------|:------------------|-------:|
| static detection of security flaws in object-oriented databases | keishi tajima | sigmod conference |   1996 |

Entity B:
| title                                                                                               | authors                                                                | venue                 |   year |
|:----------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:----------------------|-------:|
| optimizing queries with universal quantification in object-oriented and object-relational databases | jens clau &#223; en , alfons kemper , guido moerkotte , klaus peithner | very large data bases |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1985
rtable_id: 1035
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 |
|:----------------------------------------------------------------------------|:------------------------------------|:--------------|-------:|
| extraction of object-oriented structures from existing relational databases | julia e. hodges , shekar ramanathan | sigmod record |   1997 |

Entity B:
| title                                           | authors                                     | venue                                         |   year |
|:------------------------------------------------|:--------------------------------------------|:----------------------------------------------|-------:|
| a taxonomy for secure object-oriented databases | martin s. olivier , sebastiaan h. von solms | acm transactions on database systems ( tods ) |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1542
rtable_id: 1317
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 |
|:-------------------------------------|:--------------------------------------|:--------|-------:|
| mining generalized association rules | ramakrishnan srikant , rakesh agrawal | vldb    |   1995 |

Entity B:
| title                                               | authors                                     | venue                                          |   year |
|:----------------------------------------------------|:--------------------------------------------|:-----------------------------------------------|-------:|
| scalable parallel data mining for association rules | eui-hong han , george karypis , vipin kumar | international conference on management of data |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2246
rtable_id: 1812
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 |
|:---------------------------------------------|:-------------|:--------------|-------:|
| open gis and on-line environmental libraries | kenn gardels | sigmod record |   1997 |

Entity B:
| title                                        | authors      | venue             |   year |
|:---------------------------------------------|:-------------|:------------------|-------:|
| open gis and on-line environmental libraries | kenn gardels | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1076
rtable_id: 1919
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 |
|:-----------------------------------------|:--------------------------------------------------------------------------------------|:--------------|-------:|
| database research at columbia university | kenneth a. ross , gail e. kaiser , luis gravano , salvatore j. stolfo , shih-fu chang | sigmod record |   1998 |

Entity B:
| title                                                       | authors                         | venue             |   year |
|:------------------------------------------------------------|:--------------------------------|:------------------|-------:|
| research in information managment at dublin city university | mark roantree , alan f. smeaton | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1139
rtable_id: 899
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 |
|:-------------|:-------------------------------------------------------------------|:------------------|-------:|
| updating xml | daniel s. weld , zachary g. ives , igor tatarinov , alon y. halevy | sigmod conference |   2001 |

Entity B:
| title        | authors                                                            | venue                                          |   year |
|:-------------|:-------------------------------------------------------------------|:-----------------------------------------------|-------:|
| updating xml | igor tatarinov , zachary g. ives , alon y. halevy , daniel s. weld | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 56
rtable_id: 263
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 |
|:----------------------------------------------------------------------|:----------------------------|:--------|-------:|
| semantic integration in heterogeneous databases using neural networks | chris clifton , wen-syan li | vldb    |   1994 |

Entity B:
| title                                                | authors                                        | venue                 |   year |
|:-----------------------------------------------------|:-----------------------------------------------|:----------------------|-------:|
| declustering databases on heterogeneous disk systems | ling tony chen , doron rotem , sidhar seshadri | very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2570
rtable_id: 1423
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 |
|:-----------------------------------------------------------------------|:--------------------------------------------------------|:--------|-------:|
| automated selection of materialized views and indexes in sql databases | surajit chaudhuri , sanjay agrawal , vivek r. narasayya | vldb    |   2000 |

Entity B:
| title                        | authors                                                                                                                                                        | venue                 |   year |
|:-----------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|-------:|
| materialized views in oracle | randall g. bello , karl dias , alan downing , james j. feenan , jr. , james l. finnerty , william d. norcott , harry sun , andrew witkowski , mohamed ziauddin | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1538
rtable_id: 771
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 |
|:------------------------------------------------------|:------------------------------|:--------|-------:|
| access path support for referential integrity in sql2 | joachim reinert , theo härder | vldb j. |   1996 |

Entity B:
| title                                                 | authors                              | venue                                                                  |   year |
|:------------------------------------------------------|:-------------------------------------|:-----------------------------------------------------------------------|-------:|
| access path support for referential integrity in sql2 | theo h &#228; rder , joachim reinert | the vldb journal -- the international journal on very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 763
rtable_id: 1204
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 |
|:---------------------------------------------------------------|:-----------------------------------------------------|:--------|-------:|
| odefs : a file system interface to an object-oriented database | narain h. gehani , william d. roome , h. v. jagadish | vldb    |   1994 |

Entity B:
| title                                | authors                                    | venue                                          |   year |
|:-------------------------------------|:-------------------------------------------|:-----------------------------------------------|-------:|
| a database interface for file update | serge abiteboul , sophie cluet , tova milo | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2183
rtable_id: 1500
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 on influential papers | richard t. snodgrass | sigmod record |   1999 |

Entity B:
| title                               | authors         | venue             |   year |
|:------------------------------------|:----------------|:------------------|-------:|
| reminiscences on influential papers | kenneth a. ross | acm sigmod record |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 907
rtable_id: 551
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 multidimensional index trees for main memory access | sang kyun cha , keunjoo kwon , kihong kim | sigmod conference |   2001 |

Entity B:
| title                                                                                | authors                                         | venue                                                                  |   year |
|:-------------------------------------------------------------------------------------|:------------------------------------------------|:-----------------------------------------------------------------------|-------:|
| optimizing multiple dimensional queries simultaneously in multidimensional databases | weifa liang , maria e. orlowska , jeffrey x. yu | the vldb journal -- the international journal on very large data bases |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 354
rtable_id: 1217
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 |
|:---------------------------------------------------------------------------------------|:-------------------------------|:--------|-------:|
| integrating a structured-text retrieval system with an object-oriented database system | jurgen annevelink , tak w. yan | vldb    |   1994 |

Entity B:
| title                                                          | authors                       | venue             |   year |
|:---------------------------------------------------------------|:------------------------------|:------------------|-------:|
| the trigs active object-oriented database system - an overview | g. kappel , w. retschitzegger | acm sigmod record |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1116
rtable_id: 225
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 |
|:---------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|:--------------|-------:|
| report from the nsf workshop on workflow and process automation in information systems | dimitrios georgakopoulos , stef joosten , marek rusinkiewicz , alexander l. wolf , walt scacchi , amit p. sheth , jack c. wileden | sigmod record |   1996 |

Entity B:
| title                                                                                                      | authors                    | venue             |   year |
|:-----------------------------------------------------------------------------------------------------------|:---------------------------|:------------------|-------:|
| report on second international workshop on advanced issues of e-commerce and web-based information systems | kun-lung wu , philip s. yu | acm sigmod record |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2574
rtable_id: 501
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 |
|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------|:--------|-------:|
| algorithms for mining association rules for binary segmentations of huge categorical databases | kunikazu yoda , hirofumi matsuzawa , takeshi tokuyama , yasuhiko morimoto , takeshi fukuda | vldb    |   1998 |

Entity B:
| title                                               | authors                                     | venue                                          |   year |
|:----------------------------------------------------|:--------------------------------------------|:-----------------------------------------------|-------:|
| scalable parallel data mining for association rules | eui-hong han , george karypis , vipin kumar | international conference on management of data |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1510
rtable_id: 1812
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 |
|:---------------------------------------------------------------------------|:----------------------------------------------------------------|:--------------|-------:|
| cache invalidation scheme for mobile computing systems with real-time data | hei-wing leung , joe chun-hung yuen , edward chan , kam-yiu lam | sigmod record |   2000 |

Entity B:
| title                                 | authors                                    | venue                                          |   year |
|:--------------------------------------|:-------------------------------------------|:-----------------------------------------------|-------:|
| data replication for mobile computers | yixiu huang , prasad sistla , ouri wolfson | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1267
rtable_id: 1738
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 |
|:-------------------------------------------|:--------------------|:--------------|-------:|
| repositories and object oriented databases | philip a. bernstein | sigmod record |   1998 |

Entity B:
| title                                                | authors                                               | venue                 |   year |
|:-----------------------------------------------------|:------------------------------------------------------|:----------------------|-------:|
| a cost model for clustered object-oriented databases | georges gardarin , jean-robert gruser , zhao-hui tang | very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 692
rtable_id: 1479
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 |
|:----------------------------------------------------------------|:--------------|:------------------|-------:|
| static detection of security flaws in object-oriented databases | keishi tajima | sigmod conference |   1996 |

Entity B:
| title                                                                            | authors                                       | venue                                                                  |   year |
|:---------------------------------------------------------------------------------|:----------------------------------------------|:-----------------------------------------------------------------------|-------:|
| cost-driven vertical class partitioning for methods in object oriented databases | chi-wai fung , kamalakar karlapalem , qing li | the vldb journal -- the international journal on very large data bases |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1985
rtable_id: 2276
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 |
|:----------------------------------------------|:----------------------------------------------------------------------------|:--------|-------:|
| ontology-based support for digital government | ahmed k. elmagarmid , athman bouguettaya , mourad ouzzani , brahim medjahed | vldb    |   2001 |

Entity B:
| title                                         | authors                                                                 | venue                 |   year |
|:----------------------------------------------|:------------------------------------------------------------------------|:----------------------|-------:|
| ontology-based support for digital government | athman bouguettaya , ahmed k. elmagarmid , brahim medjahed , m. ouzzani | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 999
rtable_id: 1312
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 |
|:-----------------------------|:------------------------------------------------|:--------|-------:|
| expiring data in a warehouse | wilburt labio , jun yang , hector garcia-molina | vldb    |   1998 |

Entity B:
| title                                            | authors                            | venue                                          |   year |
|:-------------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| microsoft terraserver : a spatial data warehouse | tom barclay , jim gray , don slutz | international conference on management of data |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 70
rtable_id: 427
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 |
|:-----------------|:--------------|:--------------|-------:|
| chair 's message | m. tamer özsu | sigmod record |   2001 |

Entity B:
| title                | authors        | venue             |   year |
|:---------------------|:---------------|:------------------|-------:|
| treasurer 's message | joachim hammer | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 704
rtable_id: 542
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 |
|:---------------------------------------------|:-------------|:----------------------|-------:|
| binding propagation in disjunctive databases | sergio greco | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 94
rtable_id: 778
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 |
|:-----------------------------------------------------------------------------------|:---------------|:------------------|-------:|
| xml data management go native or spruce up relational systems ? ( panel abstract ) | per-åke larson | sigmod conference |   2001 |

Entity B:
| title                                                         | authors                                    | venue             |   year |
|:--------------------------------------------------------------|:-------------------------------------------|:------------------|-------:|
| relational data sharing in peer-based data management systems | beng chin ooi , yanfeng shu , kian-lee tan | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 48
rtable_id: 2240
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 |
|:-------------------------------------|:--------------------------------------------------|:------------------|-------:|
| vqbd : exploring semistructured data | jihwang yeo , sudarshan s. chawathe , thomas baby | sigmod conference |   2001 |

Entity B:
| title                                                       | authors                                                                      | venue             |   year |
|:------------------------------------------------------------|:-----------------------------------------------------------------------------|:------------------|-------:|
| lore : a database management system for semistructured data | jason mchugh , serge abiteboul , roy goldman , dallas quass , jennifer widom | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 747
rtable_id: 1758
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 logical foundation for deductive object-oriented databases | mengchi liu , tok wang ling , gillian dobbie | acm trans . database syst . |   2002 |

Entity B:
| title                                      | authors             | venue             |   year |
|:-------------------------------------------|:--------------------|:------------------|-------:|
| repositories and object oriented databases | philip a. bernstein | acm sigmod record |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2302
rtable_id: 1761
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 mining techniques | jiawei han | sigmod conference |   1996 |

Entity B:
| title                                            | authors                                                              | venue                 |   year |
|:-------------------------------------------------|:---------------------------------------------------------------------|:----------------------|-------:|
| scalable techniques for mining causal structures | craig silverstein , sergey brin , rajeev motwani , jeffrey d. ullman | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1541
rtable_id: 970
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 |
|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------|-------:|
| carnot and infosleuth : database technology and the world wide web | k. ong , christine tomlinson , darrell woelk , c. unnikrishnan , william bohrer , nigel jacobs | sigmod conference |   1995 |

Entity B:
| title                                                 | authors                                               | venue             |   year |
|:------------------------------------------------------|:------------------------------------------------------|:------------------|-------:|
| applying database visualization to the world wide web | masum z. hasan , alberto o. mendelzon , dimitra vista | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1990
rtable_id: 1902
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 on influential papers | james r. hamilton , limsoon wong , kenneth a. ross , laks v. s. lakshmanan | sigmod record |   2001 |

Entity B:
| title              | authors   | venue             |   year |
|:-------------------|:----------|:------------------|-------:|
| influential papers | ken ross  | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 968
rtable_id: 924
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 |
|:---------------------------------------------------------------|:-----------------------------------------------------|:--------|-------:|
| quality-driven integration of heterogenous information systems | johann christoph freytag , felix naumann , ulf leser | vldb    |   1999 |

Entity B:
| title                                                                           | authors                     | venue                                          |   year |
|:--------------------------------------------------------------------------------|:----------------------------|:-----------------------------------------------|-------:|
| semint : a system prototype for semantic integration in heterogeneous databases | wen-syan li , chris clifton | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1011
rtable_id: 1521
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 |
|:-------------------------------------------------------------|:----------------|:--------|-------:|
| very large databases in a commercial application environment | karl-heinz hess | vldb    |   1996 |

Entity B:
| title                                                        | authors         | venue                 |   year |
|:-------------------------------------------------------------|:----------------|:----------------------|-------:|
| very large databases in a commercial application environment | karl-heinz hess | very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2460
rtable_id: 2159
label: 1