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 |
|:---------------------------------------------------------|:----------------------------------------------------|:--------|-------:|
| efficient schemes for managing multiversionxml documents | shu-yao chien , carlo zaniolo , vassilis j. tsotras | vldb j. |   2002 |

Entity B:
| title                                                                | authors                                             | venue                 |   year |
|:---------------------------------------------------------------------|:----------------------------------------------------|:----------------------|-------:|
| efficient management of multiversion documents by object referencing | shu-yao chien , vassilis j. tsotras , carlo zaniolo | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2125
rtable_id: 2193
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 research at arizona state university | suzanne w. dietrich , forouzan golshani , susan darling urban | sigmod record |   1996 |

Entity B:
| title                                                   | authors                                                                                                           | venue             |   year |
|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| information systems research at george mason university | sushil jajodia , daniel barbar &#225; , alex brodsky , larry kerschberg , ami motro , edgar sibley , x. sean wang | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1020
rtable_id: 1746
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 |
|:-------------------------------|:--------------|:--------------|-------:|
| describing semistructured data | luca cardelli | sigmod record |   2001 |

Entity B:
| title                                                                | authors                                             | venue             |   year |
|:---------------------------------------------------------------------|:----------------------------------------------------|:------------------|-------:|
| semistructured and structured data in the web : going back and forth | paolo atzeni , giansalvatore mecca , paolo merialdo | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1310
rtable_id: 1754
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 reorganization of sparsely-populated b + trees | betty salzberg , chendong zou | sigmod conference |   1996 |

Entity B:
| title                                                    | authors                       | venue                                          |   year |
|:---------------------------------------------------------|:------------------------------|:-----------------------------------------------|-------:|
| on-line reorganization of sparsely-populated b + - trees | chendong zou , betty salzberg | international conference on management of data |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 151
rtable_id: 1960
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 |
|:---------------------------------------------------|:-------------------------------|:------------------|-------:|
| stream processing of xpath queries with predicates | ashish kumar gupta , dan suciu | sigmod conference |   2003 |

Entity B:
| title                                              | authors                        | venue                                          |   year |
|:---------------------------------------------------|:-------------------------------|:-----------------------------------------------|-------:|
| stream processing of xpath queries with predicates | ashish kumar gupta , dan suciu | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1211
rtable_id: 2040
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 |
|:----------------------------------|:-------------------------------|:--------|-------:|
| watermarking relational databases | rakesh agrawal , jerry kiernan | vldb    |   2002 |

Entity B:
| title                                                               | authors                                                                                                   | venue                                          |   year |
|:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| storing and querying ordered xml using a relational database system | igor tatarinov , stratis d. viglas , kevin beyer , jayavel shanmugasundaram , eugene shekita , chun zhang | international conference on management of data |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1771
rtable_id: 689
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 multimedia presentation algebra | maria luisa sapino , sibel adali , v. s. subrahmanian | sigmod conference |   1999 |

Entity B:
| title                             | authors                                      | venue                                          |   year |
|:----------------------------------|:---------------------------------------------|:-----------------------------------------------|-------:|
| a multimedia presentation algebra | s. adali , m. l. sapino , v. s. subrahmanian | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 232
rtable_id: 213
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 |
|:----------------------------------------------------|:--------------------------------|:--------|-------:|
| quickstore : a high performance mapped object store | seth j. white , david j. dewitt | vldb j. |   1995 |

Entity B:
| title                                               | authors                         | venue                                                                  |   year |
|:----------------------------------------------------|:--------------------------------|:-----------------------------------------------------------------------|-------:|
| quickstore : a high performance mapped object store | seth j. white , david j. dewitt | the vldb journal -- the international journal on very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1648
rtable_id: 614
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 |
|:------------------------------------------------------------------------------------|:-------------------------------|:--------|-------:|
| an almost-serial protocol for transaction execution in main-memory database systems | stephen blott , henry f. korth | vldb    |   2002 |

Entity B:
| title                                                 | authors                              | venue                                          |   year |
|:------------------------------------------------------|:-------------------------------------|:-----------------------------------------------|-------:|
| recovery protocols for shared memory database systems | lory d. molesky , krithi ramamritham | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 984
rtable_id: 1594
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 |
|:-------------------------------------------------------------------|:-------------------------------------------------------|:--------------|-------:|
| semantic integration of semistructured and structured data sources | sonia bergamaschi , maurizio vincini , silvana castano | sigmod record |   1999 |

Entity B:
| title                                            | authors                                       | venue                 |   year |
|:-------------------------------------------------|:----------------------------------------------|:----------------------|-------:|
| type classification of semi-structured documents | markus tresch , neal palmer , allen luniewski | very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1385
rtable_id: 1107
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 |
|:-------------------------------------------------------------------------|:-------------------------------------------------------------|:------------------|-------:|
| using quantitative information for efficient association rule generation | b. p &#244; ssas , m. carvalho , r. resende , w. meita , jr. | acm sigmod record |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 863
rtable_id: 448
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 |
|:-----------------------------------------------------|:------------------------------------------------------------------|:------------------|-------:|
| efficient maintenance of materialized mediated views | james j. lu , guido moerkotte , joachim schü , v. s. subrahmanian | sigmod conference |   1995 |

Entity B:
| title                                                                   | authors                                                                           | venue                 |   year |
|:------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------|-------:|
| incremental maintenance for materialized views over semistructured data | serge abiteboul , jason mchugh , michael rys , vasilis vassalos , janet l. wiener | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2261
rtable_id: 986
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 |
|:----------------------------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------|-------:|
| hyperstorm-administering structured documents using object-oriented database technology | klemens b &#246; hm , karl aberer | international conference on management of data |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1116
rtable_id: 253
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 |
|:----------------------------------------------------|:------------------------------------------|:--------|-------:|
| approximate query processing : taming the terabytes | minos n. garofalakis , phillip b. gibbons | vldb    |   2001 |

Entity B:
| title                                         | authors                                            | venue                                          |   year |
|:----------------------------------------------|:---------------------------------------------------|:-----------------------------------------------|-------:|
| approximate join processing over data streams | abhinandan das , johannes gehrke , mirek riedewald | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 713
rtable_id: 2078
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 |
|:-----------------------------------------------------|:------------------|:------------------|-------:|
| improving the efficiency of database-system teaching | jeffrey d. ullman | sigmod conference |   2003 |

Entity B:
| title                                                     | authors                                             | venue             |   year |
|:----------------------------------------------------------|:----------------------------------------------------|:------------------|-------:|
| improving timeliness in real-time secure database systems | sang h. son , rasikan david , bhavani thuraisingham | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 528
rtable_id: 378
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 |
|:-----------------------------------------|:--------------|:------------------|-------:|
| mpeg-7 standard for multimedia databases | john r. smith | sigmod conference |   2001 |

Entity B:
| title                                                                   | authors                            | venue                                          |   year |
|:------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| a user-centered interface for querying distributed multimedia databases | isabel f. cruz , kimberly m. james | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2272
rtable_id: 1
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 |
|:---------------------------------------------------|:-------------------------------------|:--------|-------:|
| transaction timestamping in ( temporal ) databases | christian s. jensen , david b. lomet | vldb    |   2001 |

Entity B:
| title                                                           | authors                                                         | venue                                          |   year |
|:----------------------------------------------------------------|:----------------------------------------------------------------|:-----------------------------------------------|-------:|
| semantic assumptions and query evaluation in temporal databases | claudio bettini , x. sean wang , elisa bertino , sushil jajodia | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2069
rtable_id: 1512
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 electronic tickets and personal guide system for public transport using mobile terminals | yahiko kambayashi , koichi goto | sigmod conference |   2003 |

Entity B:
| title                                                                                                   | authors                         | venue                                          |   year |
|:--------------------------------------------------------------------------------------------------------|:--------------------------------|:-----------------------------------------------|-------:|
| integration of electronic tickets and personal guide system for public transport using mobile terminals | koichi goto , yahiko kambayashi | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 888
rtable_id: 2006
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 |
|:-----------------------------------------------------|:-----------------------------------------------------------------------|:------------------|-------:|
| the ccube constraint object-oriented database system | alexander brodsky , pavel a. exarkhopoulo , victor e. segal , jia chen | sigmod conference |   1999 |

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



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 973
rtable_id: 2239
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 |
|:----------------------------------------------------|:----------|:----------------------------|-------:|
| distributed query evaluation on semistructured data | dan suciu | acm trans . database syst . |   2002 |

Entity B:
| title                                               | authors                                                           | venue                                          |   year |
|:----------------------------------------------------|:------------------------------------------------------------------|:-----------------------------------------------|-------:|
| qursed : querying and reporting semistructured data | yannis papakonstantinou , michalis petropoulos , vasilis vassalos | international conference on management of data |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1480
rtable_id: 688
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 |
|:----------------------------|:----------------------------|:--------------|-------:|
| metadata in video databases | ramesh jain , arun hampapur | sigmod record |   1994 |

Entity B:
| title                       | authors                     | venue             |   year |
|:----------------------------|:----------------------------|:------------------|-------:|
| metadata in video databases | ramesh jain , arun hampapur | acm sigmod record |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1206
rtable_id: 1728
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 |
|:---------------------------------|:-----------------------------------------------------------------------|:------------------|-------:|
| range queries in olap data cubes | ching-tien ho , rakesh agrawal , nimrod megiddo , ramakrishnan srikant | sigmod conference |   1997 |

Entity B:
| title                    | authors                                     | venue                 |   year |
|:-------------------------|:--------------------------------------------|:----------------------|-------:|
| temporal queries in olap | alberto o. mendelzon , alejandro a. vaisman | very large data bases |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 261
rtable_id: 1384
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 ultra highly available dbms | svein-olaf hvasshovd , svein erik bratsberg , øystein torbjørnsen | vldb    |   2000 |

Entity B:
| title                                         | authors                                | venue                 |   year |
|:----------------------------------------------|:---------------------------------------|:----------------------|-------:|
| online scaling in a highly available database | svein erik bratsberg , rune humborstad | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 339
rtable_id: 1333
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 modelling systems for environmental management information systems | david j. abel , dean kuo , kerry l. taylor | sigmod record |   1997 |

Entity B:
| title                                                                                        | authors                                                      | venue             |   year |
|:---------------------------------------------------------------------------------------------|:-------------------------------------------------------------|:------------------|-------:|
| query previews for networked information systems : a case study with nasa environmental data | khoa doan , catherine plaisant , ben shneiderman , tom bruns | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1361
rtable_id: 1927
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 |
|:---------------------------------------------|:--------------|:--------------|-------:|
| building database-driven electronic catalogs | sherif danish | sigmod record |   1998 |

Entity B:
| title                                                          | authors                                                                                 | venue                                          |   year |
|:---------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| enabling dynamic content caching for database-driven web sites | k. sel &#231; uk candan , wen-syan li , qiong luo , wang-pin hsiung , divyakant agrawal | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 607
rtable_id: 269
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 |   2003 |

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



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1452
rtable_id: 904
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 |
|:---------------------------------------------|:------------------|:------------------|-------:|
| high availability of commercial applications | kestutis ivinskis | sigmod conference |   1995 |

Entity B:
| title                                        | authors           | venue                                          |   year |
|:---------------------------------------------|:------------------|:-----------------------------------------------|-------:|
| high availability of commercial applications | kestutis ivinskis | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 494
rtable_id: 1492
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 |
|:-----------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:--------|-------:|
| 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 |
|:----------------------------------------------------|:------------|:------------------|-------:|
| the mariposa distributed database management system | jeff sidell | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 508
rtable_id: 1898
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 network is the database : data management for highly distributed systems | michael j. wynblatt , julio c. navas | sigmod conference |   2001 |

Entity B:
| title                                                                             | authors                                                     | venue                                          |   year |
|:----------------------------------------------------------------------------------|:------------------------------------------------------------|:-----------------------------------------------|-------:|
| mocha : a self-extensible database middleware system for distributed data sources | manuel rodr &#237; guez-mart &#237; nez , nick roussopoulos | international conference on management of data |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2098
rtable_id: 77
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 |
|:---------------------------------------------------------------------------------------------|:-------------------------------------------------|:----------------------------|-------:|
| using semantic values to falilitate interoperability among heterogeneous information systems | edward sciore , michael siegel , arnon rosenthal | acm trans . database syst . |   1994 |

Entity B:
| title                                                                                        | authors                                          | venue                                         |   year |
|:---------------------------------------------------------------------------------------------|:-------------------------------------------------|:----------------------------------------------|-------:|
| using semantic values to facilitate interoperability among heterogeneous information systems | edward sciore , michael siegel , arnon rosenthal | acm transactions on database systems ( tods ) |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1112
rtable_id: 1299
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 |
|:----------------------------------------|:-----------------|:------------------|-------:|
| open object database management systems | josé a. blakeley | sigmod conference |   1994 |

Entity B:
| title                        | authors                            | venue                                          |   year |
|:-----------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| data grid management systems | arun jagatheesan , arcot rajasekar | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 987
rtable_id: 2250
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 |
|:---------------------------------------|:-------------|:--------------|-------:|
| mpeg-7 and multimedia database systems | harald kosch | sigmod record |   2002 |

Entity B:
| title                                               | authors                          | venue                                                                  |   year |
|:----------------------------------------------------|:---------------------------------|:-----------------------------------------------------------------------|-------:|
| secure buffering in firm real-time database systems | binto george , jayant r. haritsa | the vldb journal -- the international journal on very large data bases |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 797
rtable_id: 1225
label: 0