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

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 |
|:----------------|:----------|:--------------|-------:|
| editor 's notes | l liu     | sigmod record |   2003 |

Entity B:
| title           | authors    | venue                                   | year   |
|:----------------|:-----------|:----------------------------------------|:-------|
| editor 's notes | rh silkman | new directions for program evaluation , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1914
rtable_id: 50887
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:------------------------------------------------------|:--------------------------------------------------------------------|:------------------|-------:|
| update propagation protocols for replicated databases | y breitbart , r komondoor , r rastogi , s seshadri , a silberschatz | sigmod conference |   1999 |

Entity B:
| title                                                | authors                 | venue   |   year |
|:-----------------------------------------------------|:------------------------|:--------|-------:|
| update replicated data during communication failures | b blaustein , o kaufman |         |   1985 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1016
rtable_id: 35405
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 queries on files | m consens , t milo | sigmod conference |   1994 |

Entity B:
| title                                          | authors                | venue                                                                 |   year |
|:-----------------------------------------------|:-----------------------|:----------------------------------------------------------------------|-------:|
| on optimizing summary-table-by-example queries | g ã?zsoyoglu , v matos | proceedings of the fourth acm sigact-sigmod symposium on & hellip ; , |   1985 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1770
rtable_id: 38682
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-----------------------------------------------------------------------------------------|:--------------|:--------|-------:|
| join index hierarchies for supporting efficient navigations in object-oriented databases | z xie , j han | vldb    |   1994 |

Entity B:
| title                                                                                   | authors       | venue                                                   | year   |
|:----------------------------------------------------------------------------------------|:--------------|:--------------------------------------------------------|:-------|
| join index hierarchies for supporting efficient navigation in object-oriented databases | z xie , j han | proc . int . conf . on very large data bases ( vldb ) , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 254
rtable_id: 3284
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------------------------------------------------------|:---------------------|:------------------|-------:|
| lh * rs : a high-availability scalable distributed data structure using reed solomon codes | w litwin , t schwarz | sigmod conference |   2000 |

Entity B:
| title                                                                                      | authors              | venue               |   year |
|:-------------------------------------------------------------------------------------------|:---------------------|:--------------------|-------:|
| lh * rs : a high-availability scalable distributed data structure using reed solomon codes | w litwin , t schwarz | acm sigmod record , |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 832
rtable_id: 3260
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 | a savasere , e omiecinski , s navathe | vldb    |   1995 |

Entity B:
| title                                                              | authors                | venue     | year   |
|:-------------------------------------------------------------------|:-----------------------|:----------|:-------|
| discovery of multiple-level association rules from large databases | h jiawei , f yong-jian | proc of , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1953
rtable_id: 61955
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 future of web services - ii | f cabrera | sigmod conference |   2003 |

Entity B:
| title                  | authors                                         | venue                |   year |
|:-----------------------|:------------------------------------------------|:---------------------|-------:|
| the future of software | p brereton , d budgen , k bennett , m munro , p | communications-acm , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 350
rtable_id: 61191
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 performance in replicated databases through relaxed coherency | r gallersdіrfer , m nicola | vldb    |   1995 |

Entity B:
| title                                               | authors                                     | venue   | year   |
|:----------------------------------------------------|:--------------------------------------------|:--------|:-------|
| improving hash join performance through prefetching | s chen , a ailamaki , pb gibbons , tc mowry |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 28
rtable_id: 37773
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:---------------------------------------------------------------------------------|:---------------------|:--------------|-------:|
| research issues for data communication in mobile ad-hoc network database systems | l fife , l gruenwald | sigmod record |   2003 |

Entity B:
| title                                                                            | authors               | venue           |   year |
|:---------------------------------------------------------------------------------|:----------------------|:----------------|-------:|
| research issues for data communication in mobile ad-hoc network database systems | ld fife , l gruenwald | sigmod record , |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1968
rtable_id: 41358
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 oasis multidatabase prototype | m roantree , j murphy , w hasselbring | sigmod record |   1999 |

Entity B:
| title                             | authors       | venue           |   year |
|:----------------------------------|:--------------|:----------------|-------:|
| the oasis multidatabase prototype | w hasselbring | sigmod record , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 423
rtable_id: 60866
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 cost model for query processing in high dimensional data spaces | c bіhm    | acm trans . database syst . |   2000 |

Entity B:
| title                                                     | authors                                          | venue               | year   |
|:----------------------------------------------------------|:-------------------------------------------------|:--------------------|:-------|
| query processing using multidimensional clustering in db2 | b bhattacharjee , s padmanabhan , t malkemus , l | proceedings of vldb |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2314
rtable_id: 17076
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                        | authors                     | venue             |   year |
|:-----------------------------|:----------------------------|:------------------|-------:|
| data grid management systems | a jagatheesan , a rajasekar | sigmod conference |   2003 |

Entity B:
| title                                          | authors    | venue                                   | year   |
|:-----------------------------------------------|:-----------|:----------------------------------------|:-------|
| analytic models for database management system | i miyamoto | proc . of int . conf . on vldb , sept , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 288
rtable_id: 35825
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 ores temporal database management system | b theodoulidis , a ait-braham , g andrianopoulos , j chaudhary , g karvelis , s sou | sigmod conference |   1994 |

Entity B:
| title                                 | authors                    | venue   | year   |
|:--------------------------------------|:---------------------------|:--------|:-------|
| the coral deductive database system . | acms anthology , p edition |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1840
rtable_id: 22349
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 monte carlo algorithm for fast projective clustering | c procopiuc , m jones , p agarwal , t murali | sigmod conference |   2002 |

Entity B:
| title                                    | authors                                               | venue               |   year |
|:-----------------------------------------|:------------------------------------------------------|:--------------------|-------:|
| fast algorithms for projected clustering | cc aggarwal , c procopiuc , jl wolf , ps yu , js park | sigmod conference , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 501
rtable_id: 25436
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 databases and information systems ( adbis ) | a caplinskas , j eder , o vasilecas | sigmod record |   2002 |

Entity B:
| title                                         | authors               | venue   | year   |
|:----------------------------------------------|:----------------------|:--------|:-------|
| advances in databases and information systems | a caplinskas , j eder |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 18
rtable_id: 41055
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:--------------------------------------------------------------------------------------------------------------------------|:----------|:--------|-------:|
| proceedings of the 2002 acm sigmod international conference on management of data , madison , wisconsin , june 3-6 , 2002 |           |         |   2002 |

Entity B:
| title                                                                                               | authors                          | venue               | year   |
|:----------------------------------------------------------------------------------------------------|:---------------------------------|:--------------------|:-------|
| discover : keyword search in relational databases in proceedings of the international conference on | v hristidis , y papakonstantinou | hang kong , china , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 454
rtable_id: 26483
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 index rebuild | n ponnekanti , h kodavalla | sigmod conference |   2000 |

Entity B:
| title                | authors                    | venue   | year   |
|:---------------------|:---------------------------|:--------|:-------|
| online index rebuild | n ponnekanti , h kodavalla |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 893
rtable_id: 29502
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                                      | authors     | venue         |   year |
|:-------------------------------------------|:------------|:--------------|-------:|
| repositories and object oriented databases | p bernstein | sigmod record |   1998 |

Entity B:
| title                                              | authors                      | venue         | year   |
|:---------------------------------------------------|:-----------------------------|:--------------|:-------|
| rule management in objectoriented database systems | o diaz , nw paton , pmd gray | proc . vldb , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 632
rtable_id: 52385
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 timeliness in real-time secure database systems | s son , r david , b thuraisingham | sigmod record |   1996 |

Entity B:
| title                                                    | authors     | venue           | year   |
|:---------------------------------------------------------|:------------|:----------------|:-------|
| special section on advances in realtime database systems | a bestavros | sigmod record , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 682
rtable_id: 61690
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 | a brodsky , v segal , j chen , p exarkhopoulo | sigmod conference |   1999 |

Entity B:
| title                                                                                   | authors               | venue   | year   |
|:----------------------------------------------------------------------------------------|:----------------------|:--------|:-------|
| an active object-oriented database : a multi-paradigm approach to constraint management | h ishikawa , k kubota |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1049
rtable_id: 47811
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:---------------------------------------------------------|:-----------------------------------------|:--------|-------:|
| warehousing workflow data : challenges and opportunities | a bonifati , f casati , u dayal , m shan | vldb    |   2001 |

Entity B:
| title                                                 | authors       | venue           | year   |
|:------------------------------------------------------|:--------------|:----------------|:-------|
| mining system audit data : opportunitiesandchallenges | w lee , w fan | sigmod record , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1272
rtable_id: 7518
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:---------------------------------------|:----------------------|:----------------------------|-------:|
| distance browsing in spatial databases | g hjaltason , h samet | acm trans . database syst . |   1999 |

Entity B:
| title                           | authors   | venue                                                                          | year   |
|:--------------------------------|:----------|:-------------------------------------------------------------------------------|:-------|
| redundancy in spatial databases | oo ja     | proc . acm sigmod int . conf . on management of data , portland , & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2372
rtable_id: 56745
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:--------------------------------------------------------|:----------------------------------------------------------------------------------------|:--------|-------:|
| efficiently publishing relational data as xml documents | j shanmugasundaram , e shekita , r barr , m carey , b lindsay , h pirahesh , b reinwald | vldb j. |   2001 |

Entity B:
| title                                                   | authors                                               | venue                                                                 |   year |
|:--------------------------------------------------------|:------------------------------------------------------|:----------------------------------------------------------------------|-------:|
| efficiently publishing relational data as xml documents | j shanmugasundaram , e shekita , r barr , m carey , b | the vldb journal the international journal on very large & hellip ; , |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2489
rtable_id: 53022
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:------------------------------------------------------|:---------------------------------------------------|:----------------------------|-------:|
| flexible support for multiple access control policies | s jajodia , p samarati , m sapino , v subrahmanian | acm trans . database syst . |   2001 |

Entity B:
| title                                                              | authors                                              | venue   | year   |
|:-------------------------------------------------------------------|:-----------------------------------------------------|:--------|:-------|
| a unified framework for enforcing multiple access control policies | s jajodia , p samarati , vs subrahmanian , e bertino |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2335
rtable_id: 63581
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------------------------------------|:--------------|:--------------|-------:|
| incremental maintenance of recursive views using relational calculus/sql | g dong , j su | sigmod record |   2000 |

Entity B:
| title                                                 | authors                      | venue   | year   |
|:------------------------------------------------------|:-----------------------------|:--------|:-------|
| incremental maintenance of schema-restructuring views | a koeller , ea rundensteiner |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 272
rtable_id: 11246
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------------------------------------------------------|:-----------------------------|:--------------|-------:|
| design and user testing of a multi-paradigm query interface to an object-oriented database | d doan , n paton , a kilgour | sigmod record |   1995 |

Entity B:
| title                                                                                   | authors               | venue   | year   |
|:----------------------------------------------------------------------------------------|:----------------------|:--------|:-------|
| an active object-oriented database : a multi-paradigm approach to constraint management | h ishikawa , k kubota |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 884
rtable_id: 47811
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:------------------------------------------------|:------------------------------------------------------------|:--------|-------:|
| vxmlr : a visual xml-relational database system | a zhou , h lu , s zheng , y liang , l zhang , w ji , z tian | vldb    |   2001 |

Entity B:
| title                                                             | authors                                                | venue                                             | year   |
|:------------------------------------------------------------------|:-------------------------------------------------------|:--------------------------------------------------|:-------|
| access path selection 111 a relational database management system | pg sehnger , mm astrahan , dd chamberhn , ra lone , tg | prcceedmgs of 1979 acm sigmod conference , ( june |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1244
rtable_id: 15611
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 | r agrawal , j kiernan | vldb    |   2002 |

Entity B:
| title                                         | authors                 | venue                                  | year   |
|:----------------------------------------------|:------------------------|:---------------------------------------|:-------|
| buer managementin relational database systems | gm sacco , m schkolnick | acm transactions on database systems , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2208
rtable_id: 31328
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:--------------------------------------------------------------|:----------------------|:--------------|-------:|
| relational data sharing in peer-based data management systems | b ooi , y shu , k tan | sigmod record |   2003 |

Entity B:
| title                                                           | authors       | venue                                                                     |   year |
|:----------------------------------------------------------------|:--------------|:--------------------------------------------------------------------------|-------:|
| an approach to implementing a relational data management system | jb rothnie jr | proceedings of the 1974 acm sigfidet ( now sigmod ) workshop & hellip ; , |   1974 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1944
rtable_id: 37229
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:----------------------|:----------|:--------|-------:|
| vice chair 's message |           |         |   1999 |

Entity B:
| title                          | authors           | venue   | year   |
|:-------------------------------|:------------------|:--------|:-------|
| fact division chair 's message | ak gupta , d mahr |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 470
rtable_id: 27009
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------------------------------------------------------|:---------------------------|:------------------|-------:|
| parallel mining algorithms for generalized association rules with classification hierarchy | t shintani , m kitsuregawa | sigmod conference |   1998 |

Entity B:
| title                                                              | authors                   | venue   | year   |
|:-------------------------------------------------------------------|:--------------------------|:--------|:-------|
| a new algorithm for faster mining of generalized association rules | j hipp , a myka , r wirth |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1213
rtable_id: 24667
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 taxonomy of correctness criteria in database applications | k ramamritham , p chrysanthis | vldb j. |   1996 |

Entity B:
| title                                                              | authors      | venue                 |   year |
|:-------------------------------------------------------------------|:-------------|:----------------------|-------:|
| a taxonomy of architectures for synchronous groupware applications | jf patterson | acm sigois bulletin , |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2508
rtable_id: 50909
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                                                                           | authors                           | venue                       |   year |
|:--------------------------------------------------------------------------------|:----------------------------------|:----------------------------|-------:|
| static analysis techniques for predicting the behavior of active database rules | a aiken , j hellerstein , j widom | acm trans . database syst . |   1995 |

Entity B:
| title                                                                                | authors                  | venue                                  |   year |
|:-------------------------------------------------------------------------------------|:-------------------------|:---------------------------------------|-------:|
| static analysis techniques for predicting the behavior of active database rules â ?? | jm hellerstein , j widom | acm transactions on database systems , |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2248
rtable_id: 56285
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------|:----------|:--------|-------:|
| scalablity and availability in oracle7 7.3 | d gawlick | vldb    |   1996 |

Entity B:
| title                                      | authors   | venue                                                                  |   year |
|:-------------------------------------------|:----------|:-----------------------------------------------------------------------|-------:|
| scalablity and availability in oracle7 7.3 | d gawlick | proceedings of the international conference on very large & hellip ; , |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2589
rtable_id: 55239
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 assumptions and query evaluation in temporal databases | c bettini , x wang , e bertino , s jajodia | sigmod conference |   1995 |

Entity B:
| title                                                                                | authors      | venue                                 |   year |
|:-------------------------------------------------------------------------------------|:-------------|:--------------------------------------|-------:|
| query evaluation and optimization in deductive and object-oriented spatial databases | w lu , j han | information and software technology , |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1603
rtable_id: 55639
label: 0