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 |
|:----------------------------------------------------|:------------------------------------------------|:--------------|-------:|
| vldb workshop on technologies in e-services ( tes ) | ming-chien shan , fabio casati , umeshwar dayal | sigmod record |   2000 |

Entity B:
| title                                                              | authors                                      | venue             |   year |
|:-------------------------------------------------------------------|:---------------------------------------------|:------------------|-------:|
| report on the vldb workshop on technologies for e-services ( tes ) | fabio casati , umesh dayal , ming-chien shan | acm sigmod record |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1654
rtable_id: 450
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 |
|:------------------------|:--------------------------------------|:------------------|-------:|
| online query processing | peter j. haas , joseph m. hellerstein | sigmod conference |   2001 |

Entity B:
| title                                                     | authors                                                      | venue                 |   year |
|:----------------------------------------------------------|:-------------------------------------------------------------|:----------------------|-------:|
| online dynamic reordering for interactive data processing | vijayshankar raman , bhaskaran raman , joseph m. hellerstein | very large data bases |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1489
rtable_id: 847
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 |
|:----------------------------------------------------|:---------------------------------|:--------|-------:|
| secure buffering in firm real-time database systems | binto george , jayant r. haritsa | vldb    |   1998 |

Entity B:
| title                                                                  | authors                                                               | venue                 |   year |
|:-----------------------------------------------------------------------|:----------------------------------------------------------------------|:----------------------|-------:|
| a database system for real-time event aggregation in telecommunication | jerry baulier , stephen blott , henry f. korth , abraham silberschatz | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 839
rtable_id: 991
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 |
|:-----------------------------|:--------------------------------------------------------------------|:--------|-------:|
| querying shapes of histories | mohamed zaït , giuseppe psaila , edward l. wimmers , rakesh agrawal | vldb    |   1995 |

Entity B:
| title                        | authors                                                                                | venue                                          |   year |
|:-----------------------------|:---------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| querying network directories | h. v. jagadish , laks v. s. lakshmanan , tova milo , divesh srivastava , dimitra vista | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1807
rtable_id: 214
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 triggers and declarative constraints in sql database sytems | roberta cochrane , hamid pirahesh , nelson mendonça mattos | vldb    |   1996 |

Entity B:
| title                                                 | authors                                                                                  | venue                 |   year |
|:------------------------------------------------------|:-----------------------------------------------------------------------------------------|:----------------------|-------:|
| integrating the ub-tree into a database system kernel | frank ramsak , volker markl , robert fenk , martin zirkel , klaus elhardt , rudolf bayer | very large data bases |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 35
rtable_id: 1376
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 dedale system for complex spatial queries | luc segoufin , philippe rigaux , stéphane grumbach | sigmod conference |   1998 |

Entity B:
| title                                              | authors                                     | venue                                          |   year |
|:---------------------------------------------------|:--------------------------------------------|:-----------------------------------------------|-------:|
| xcache : a semantic caching system for xml queries | li chen , elke a. rundensteiner , song wang | international conference on management of data |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 959
rtable_id: 715
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 |
|:---------------------------------------------------|:------------|:--------------|-------:|
| converting relational to object-oriented databases | joseph fong | sigmod record |   1997 |

Entity B:
| title                                                                    | authors       | venue                                                                  |   year |
|:-------------------------------------------------------------------------|:--------------|:-----------------------------------------------------------------------|-------:|
| versioning and configuration management in an object-oriented data model | edward sciore | the vldb journal -- the international journal on very large data bases |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 287
rtable_id: 582
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 nthu and itri | arbee l. p. chen | sigmod record |   1994 |

Entity B:
| title                                           | authors                                                                                                                 | venue             |   year |
|:------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| database research at the university of oklahoma | le gruenwald , leonard brown , ravi dirckze , sylvain guinepain , carlos sanchez , brian summers , sirirut vanichayobon | acm sigmod record |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2610
rtable_id: 93
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 |
|:------------------------------------------|:--------------------------------------------------|:--------------|-------:|
| analysis of sigmod 's co-authorship graph | mario a. nascimento , jörg sander , jeffrey pound | sigmod record |   2003 |

Entity B:
| title                                     | authors                                                  | venue             |   year |
|:------------------------------------------|:---------------------------------------------------------|:------------------|-------:|
| analysis of sigmod 's co-authorship graph | mario a. nascimento , j &#246; rg sander , jeffrey pound | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1911
rtable_id: 2258
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 |
|:----------------------------------------------|:--------------------------------|:------------------|-------:|
| temporal aggregation in active database rules | iakovos motakis , carlo zaniolo | sigmod conference |   1997 |

Entity B:
| title                                                                    | authors                         | venue                                          |   year |
|:-------------------------------------------------------------------------|:--------------------------------|:-----------------------------------------------|-------:|
| temporal conditions and integrity constraints in active database systems | a. prasad sistla , ouri wolfson | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2209
rtable_id: 1511
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 |
|:-------------------------------------------------------------------------------------------|:------------------------------------------------------|:--------|-------:|
| cost-based selection of path expression processing algorithms in object-oriented databases | zhao-hui tang , georges gardarin , jean-robert gruser | vldb    |   1996 |

Entity B:
| title                                                                 | authors                            | venue                                          |   year |
|:----------------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| integration of spatial join algorithms for processing multiple inputs | nikos mamoulis , dimitris papadias | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 3
rtable_id: 219
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 predicate-based caching scheme for client-server database architectures | julie basu , arthur m. keller | vldb j. |   1996 |

Entity B:
| title                                                                     | authors                    | venue                                          |   year |
|:--------------------------------------------------------------------------|:---------------------------|:-----------------------------------------------|-------:|
| aries/csa : a method for database recovery in client-server architectures | c. mohan , inderpal narang | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2410
rtable_id: 1742
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 |
|:-----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:--------|-------:|
| enabling end-users to construct data-intensive web-sites from xml repositories : an example-based approach | hiroyuki kitagawa , satoshi takano , seiichi koizumi , atsuyuki morishima | vldb    |   2001 |

Entity B:
| title                                                                                                      | authors                                                                   | venue                 |   year |
|:-----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:----------------------|-------:|
| enabling end-users to construct data-intensive web-sites from xml repositories : an example-based approach | atsuyuki morishima , seiichi koizumi , hiroyuki kitagawa , satoshi takano | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1373
rtable_id: 1294
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 |
|:------------------------------------------------|:---------------|:--------------|-------:|
| advances in real-time database systems research | azer bestavros | sigmod record |   1996 |

Entity B:
| title                                                            | authors                                                                          | venue             |   year |
|:-----------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------|-------:|
| deeds towards a distributed and active real-time database system | s. f. andler , j. hansson , j. eriksson , j. mellin , m. berndtsson , b. eftring | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2178
rtable_id: 379
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 |
|:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|:--------|-------:|
| mariposa : a wide-area distributed database system | carl staelin , paul m. aoki , witold litwin , michael stonebraker , adam sah , jeff sidell , andrew yu , avi pfeffer | vldb j. |   1996 |

Entity B:
| title                                             | authors                                                                                                                                                                          | venue                 |   year |
|:--------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|-------:|
| smooth - a distributed multimedia database system | harald kosch , l &#225; szl &#243; b &#246; sz &#246; rm &#233; nyi , alexander bachlechner , christian hanin , christian hofbauer , margit lang , carmen riedler , roland tusch | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1498
rtable_id: 1340
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 spatial join algorithms for processing multiple inputs | nikos mamoulis , dimitris papadias | sigmod conference |   1999 |

Entity B:
| title                                                      | authors                                 | venue                                          |   year |
|:-----------------------------------------------------------|:----------------------------------------|:-----------------------------------------------|-------:|
| incremental distance join algorithms for spatial databases | g &#237; sli r. hjaltason , hanan samet | international conference on management of data |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 269
rtable_id: 1802
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 |
|:----------------------------------------|:----------------------------------------------------------------|:--------|-------:|
| grid data management systems & services | reagan moore , arun jagatheesan , paul watson , norman w. paton | vldb    |   2003 |

Entity B:
| title                                                          | authors                            | venue                                          |   year |
|:---------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| dynamat : a dynamic view management system for data warehouses | yannis kotidis , nick roussopoulos | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1546
rtable_id: 53
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 |
|:------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|:--------------|-------:|
| www-udk : a web-based environmental meta-information system | rudolf legat , arne koschel , andree keitel , ralf kramer , claudia rolker , ralf nikolai , peter c. lockemann , konrad zirm | 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: 1551
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 |
|:-----------------------------------------------------------------------|:----------------------------------------------|:--------|-------:|
| approximate query translation across heterogeneous information sources | hector garcia-molina , kevin chen-chuan chang | vldb    |   2000 |

Entity B:
| title                                             | authors                             | venue                 |   year |
|:--------------------------------------------------|:------------------------------------|:----------------------|-------:|
| merging ranks from heterogeneous internet sources | luis gravano , hector garcia-molina | very large data bases |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 279
rtable_id: 943
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 |
|:----------------------------------------------|:---------------------------------------------|:------------------|-------:|
| similarity query processing using disk arrays | apostolos papadopoulos , yannis manolopoulos | sigmod conference |   1998 |

Entity B:
| title                                  | authors                               | venue                                          |   year |
|:---------------------------------------|:--------------------------------------|:-----------------------------------------------|-------:|
| query processing techniques for arrays | arunprasad p. marathe , kenneth salem | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2367
rtable_id: 199
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

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

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

Entity A:
| title                                                            | authors                                             | venue   |   year |
|:-----------------------------------------------------------------|:----------------------------------------------------|:--------|-------:|
| dynamic load balancing in hierarchical parallel database systems | luc bouganim , daniela florescu , patrick valduriez | vldb    |   1996 |

Entity B:
| title                                                         | authors                                            | venue                                                                  |   year |
|:--------------------------------------------------------------|:---------------------------------------------------|:-----------------------------------------------------------------------|-------:|
| data partitioning and load balancing in parallel disk systems | peter scheuermann , gerhard weikum , peter zabback | the vldb journal -- the international journal on very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 391
rtable_id: 1250
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 search of multi-dimensional b-trees | dave birdsall , hedieh yaghmai , harry leslie , rohit jain | vldb    |   1995 |

Entity B:
| title                                        | authors       | venue                                                                  |   year |
|:---------------------------------------------|:--------------|:-----------------------------------------------------------------------|-------:|
| management of multidimensional discrete data | peter baumann | the vldb journal -- the international journal on very large data bases |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1263
rtable_id: 586
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 on the 8th international workshop on knowledge representation meets databases ( krdb ) | dan suciu , maurizio lenzerini , daniele nardi , werner nutt | sigmod record |   2001 |

Entity B:
| title                                                                                                                            | authors                                          | venue             |   year |
|:---------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------|:------------------|-------:|
| intelligent access to heterogeneous information sources : report on the 4th workshop on knowledge representation meets databases | franz baader , manfred a. jeusfeld , werner nutt | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 156
rtable_id: 1763
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 |
|:----------------------------------------------------------------------|:-------------------------------------------------|:------------------|-------:|
| eliminating costly redundant computations from sql trigger executions | françois llirbat , françoise fabret , eric simon | sigmod conference |   1997 |

Entity B:
| title                                                                 | authors                                                        | venue                                          |   year |
|:----------------------------------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------|-------:|
| eliminating costly redundant computations from sql trigger executions | fran &#231; ois llirbat , fran &#231; oise fabret , eric simon | international conference on management of data |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 613
rtable_id: 1826
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 |
|:-------------------------------------------------------------------------------------|:------------------------------------------------|:--------|-------:|
| optimizing multiple dimensional queries simultaneously in multidimensional databases | jeffrey xu yu , weifa liang , maria e. orlowska | vldb j. |   2000 |

Entity B:
| title                                                                    | authors                                                               | venue                                          |   year |
|:-------------------------------------------------------------------------|:----------------------------------------------------------------------|:-----------------------------------------------|-------:|
| simultaneous optimization and evaluation of multiple dimensional queries | yihong zhao , prasad m. deshpande , jeffrey f. naughton , amit shukla | international conference on management of data |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2062
rtable_id: 1801
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

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

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

Entity A:
| title                                             | authors                                    | venue             |   year |
|:--------------------------------------------------|:-------------------------------------------|:------------------|-------:|
| query optimization in compressed database systems | zhiyuan chen , flip korn , johannes gehrke | sigmod conference |   2001 |

Entity B:
| title                                | authors           | venue                                          |   year |
|:-------------------------------------|:------------------|:-----------------------------------------------|-------:|
| query optimization at the crossroads | surajit chaudhuri | international conference on management of data |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1892
rtable_id: 1855
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 |
|:-------------|:----------|:--------|-------:|
| author index | ?         | vldb    |   2000 |

Entity B:
| title        | authors   | venue                 |   year |
|:-------------|:----------|:----------------------|-------:|
| author index |           | very large data bases |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1710
rtable_id: 2136
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 |
|:---------------------------------------------------|:------------|:--------------|-------:|
| converting relational to object-oriented databases | joseph fong | sigmod record |   1997 |

Entity B:
| title                                        | authors          | venue                                          |   year |
|:---------------------------------------------|:-----------------|:-----------------------------------------------|-------:|
| query unnesting in object-oriented databases | leonidas fegaras | international conference on management of data |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 287
rtable_id: 1787
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 on the 1995 international workshop on temporal databases | arie segev , richard t. snodgrass , christian s. jensen | sigmod record |   1995 |

Entity B:
| title                                                                                                  | authors                                                             | venue             |   year |
|:-------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:------------------|-------:|
| report on the 5th international workshop on the design and management of data warehouses ( dmdw ' 03 ) | hans j. lenz , panos vassiliadis , manfred jeusfeld , martin staudt | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1015
rtable_id: 2210
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 in object databases | s. sudarshan , rajeev rastogi , s. seshadri , mohana krishna lakhamraju | sigmod conference |   2000 |

Entity B:
| title                                                       | authors          | venue                                                                  |   year |
|:------------------------------------------------------------|:-----------------|:-----------------------------------------------------------------------|-------:|
| enhanced abstract data types in object-relational databases | praveen seshadri | the vldb journal -- the international journal on very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1741
rtable_id: 1179
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

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

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

Entity A:
| title                                                                  | authors                      | venue         |   year |
|:-----------------------------------------------------------------------|:-----------------------------|:--------------|-------:|
| unisql 's next-generation object-relational database management system | phil janus , albert d'andrea | sigmod record |   1996 |

Entity B:
| title                                          | authors                                                                                     | venue                                                                  |   year |
|:-----------------------------------------------|:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|-------:|
| anatomy of a native xml base management system | t. fiebig , s. helmer , c.-c . kanne , g. moerkotte , j. neumann , r. schiele , t. westmann | the vldb journal -- the international journal on very large data bases |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 9
rtable_id: 1233
label: 0