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 |
|:----------------------------------------------------------------------------------------------|:----------|:--------|-------:|
| industrial panel on data warehousing technologies : experiences , challenges , and directions | u dayal   | vldb    |   1999 |

Entity B:
| title                                                                                         | authors   | venue                                                                 |   year |
|:----------------------------------------------------------------------------------------------|:----------|:----------------------------------------------------------------------|-------:|
| industrial panel on data warehousing technologies : experiences , challenges , and directions | u dayal   | proceedings of the 25th international conference on very & hellip ; , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1609
rtable_id: 7292
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 |
|:------------------------------------|:---------------------------------------|:------------------|-------:|
| implementing data cubes efficiently | v harinarayan , a rajaraman , j ullman | sigmod conference |   1996 |

Entity B:
| title                                              | authors         | venue                                                               | year   |
|:---------------------------------------------------|:----------------|:--------------------------------------------------------------------|:-------|
| rajaraman . a. implementing data cubes efficiently | ujd harinarayan | proc acm sigmod int &#39; l conf on management of data . montreal , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1534
rtable_id: 23240
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 |
|:-----------------|:----------|:--------|-------:|
| chair 's message |           |         |   1998 |

Entity B:
| title                | authors   | venue   |   year |
|:---------------------|:----------|:--------|-------:|
| treasurer 's message | j hammer  | sigmod  |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 622
rtable_id: 45743
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 platform based on the multi-dimensional data model for analysis of bio-molecular structures | s srinivasa , s kumar | vldb    |   2003 |

Entity B:
| title                                                                  | authors   | venue   |   year |
|:-----------------------------------------------------------------------|:----------|:--------|-------:|
| a simulation model for performance analysis of large shared data bases | c hulten  | vldb ,  |   1977 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2023
rtable_id: 17279
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 |
|:--------------------------------------------------------|:-------------------|:------------------|-------:|
| time-parameterized queries in spatio-temporal databases | y tao , d papadias | sigmod conference |   2002 |

Entity B:
| title                                                   | authors            | venue               |   year |
|:--------------------------------------------------------|:-------------------|:--------------------|-------:|
| time-parameterized queries in spatio-temporal databases | y tao , d papadias | sigmod conference , |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 537
rtable_id: 25824
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 |
|:--------------------------|:------------------------------------------------------------------------------------------------------|:------------------|-------:|
| metu object-oriented dbms | a dogac , i arpinar , c evrendilek , c ozkan , i altintas , i durusoy , m altinel , t okay , y saygin | sigmod conference |   1994 |

Entity B:
| title                               | authors             | venue                               |   year |
|:------------------------------------|:--------------------|:------------------------------------|-------:|
| a logic of object-oriented programs | m abadi , krm leino | lecture notes in computer science , |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1852
rtable_id: 14741
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 |
|:--------------------------------------------------|:----------------------------------------------------------------------------------------------|:--------|-------:|
| smooth - a distributed multimedia database system | h kosch , l bіszіrmщnyi , a bachlechner , c hanin , c hofbauer , m lang , c riedler , r tusch | vldb    |   2001 |

Entity B:
| title                                     | authors      | venue   | year   |
|:------------------------------------------|:-------------|:--------|:-------|
| parallel and distributed database systems | a procedures |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1239
rtable_id: 23457
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 |
|:-----------------------|:-----------------------|:--------|-------:|
| indexing multiple sets | c kilger , g moerkotte | vldb    |   1994 |

Entity B:
| title                  | authors                           | venue                       | year   |
|:-----------------------|:----------------------------------|:----------------------------|:-------|
| indexing moving points | pk argarwal , l arge , j erickson | proc . of acm podsâ ?? 00 , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 154
rtable_id: 29539
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 |
|:-------------------------------------------------|:-----------------------|:--------|-------:|
| inferring function semantics to optimize queries | m cherniack , s zdonik | vldb    |   1998 |

Entity B:
| title                                            | authors                 | venue                                                                  |   year |
|:-------------------------------------------------|:------------------------|:-----------------------------------------------------------------------|-------:|
| inferring function semantics to optimize queries | m cherniack , sb zdonik | proceedings of the international conference on very large & hellip ; , |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1595
rtable_id: 29811
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 |
|:-----------------------------------------|:------------------------------------------|:--------|-------:|
| recovering information from summary data | c faloutsos , h jagadish , n sidiropoulos | vldb    |   1997 |

Entity B:
| title                                   | authors                                     | venue   | year   |
|:----------------------------------------|:--------------------------------------------|:--------|:-------|
| recovering informationfrom summary data | c faloutsos , hv jagadish , nd sidiropoulos |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1884
rtable_id: 55688
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 |
|:-----------------------------------------------|:------------------------|:------------------|-------:|
| s3 : similarity search in cad database systems | s berchtold , h kriegel | sigmod conference |   1997 |

Entity B:
| title                         | authors                                   | venue                                     | year   |
|:------------------------------|:------------------------------------------|:------------------------------------------|:-------|
| proximity search in databases | g roy , s narayanan , v suresh , g hector | proceedings of the 24th vldb conference , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1368
rtable_id: 8133
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 |
|:--------------------------------------------------------------------------|:---------------------------------|:------------------|-------:|
| structures for manipulating proposed updates in object-oriented databases | m doherty , r hull , m rupawalla | sigmod conference |   1996 |

Entity B:
| title                                                                     | authors                          | venue               |   year |
|:--------------------------------------------------------------------------|:---------------------------------|:--------------------|-------:|
| structures for manipulating proposed updates in object-oriented databases | m doherty , r hull , m rupawalla | sigmod conference , |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1529
rtable_id: 27863
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 structured approach for the definition of the semantics of active databases | p fraternali , l tanca | acm trans . database syst . |   1995 |

Entity B:
| title                                                        | authors                  | venue                               |   year |
|:-------------------------------------------------------------|:-------------------------|:------------------------------------|-------:|
| a uniform approach for the definition of security properties | r focardi , f martinelli | lecture notes in computer science , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2263
rtable_id: 6026
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 |
|:-------------------------------------------------------------------------------------------|:-------------------------------|:--------|-------:|
| cost-based selection of path expression processing algorithms in object-oriented databases | g gardarin , j gruser , z tang | vldb    |   1996 |

Entity B:
| title                                                | authors           | venue               |   year |
|:-----------------------------------------------------|:------------------|:--------------------|-------:|
| functional completeness in object-oriented databases | p mishra , m eich | acm sigmod record , |   1992 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1794
rtable_id: 41942
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 |
|:--------------------------------------------------------------------------------------------------------------|:-------------------|:--------|-------:|
| integrating symbolic images into a multimedia database system using classification and abstraction approaches | a soffer , h samet | vldb j. |   1998 |

Entity B:
| title                                                                                              | authors            | venue                                                                 |   year |
|:---------------------------------------------------------------------------------------------------|:-------------------|:----------------------------------------------------------------------|-------:|
| integrating symbolic images into a multimedia database system using classification and abstraction | a soffer , h samet | the vldb journal the international journal on very large & hellip ; , |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2430
rtable_id: 1866
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 |
|:---------------------------------------------------|:------------------------------------------|:--------|-------:|
| locating data sources in large distributed systems | l galanis , y wang , s jeffery , d dewitt | vldb    |   2003 |

Entity B:
| title                                                | authors              | venue                                  |   year |
|:-----------------------------------------------------|:---------------------|:---------------------------------------|-------:|
| achieving robustness in distributed database systems | dl eager , kc sevcik | acm transactions on database systems , |   1983 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2030
rtable_id: 20709
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 |
|:-----------------------------------------------------------------------------------------------------|:--------------------|:--------|-------:|
| integrated document caching and prefetching in storage hierarchies based on markov-chain predictions | a kraiss , g weikum | vldb j. |   1998 |

Entity B:
| title                                                                                     | authors             | venue              | year   |
|:------------------------------------------------------------------------------------------|:--------------------|:-------------------|:-------|
| integrated document prefetching and caching in hierarchical storage based on markov-chain | a kraiss , g weikum | vldb journal ( may |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2429
rtable_id: 22625
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 |
|:----------------|:----------|:--------------|-------:|
| editor 's notes | j widom   | sigmod record |   1996 |

Entity B:
| title           | authors                | venue                                      | year   |
|:----------------|:-----------------------|:-------------------------------------------|:-------|
| editors ' notes | c knapper , s piccinin | new directions for teaching and learning , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 661
rtable_id: 43210
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 impress ddt : a database design toolbox based on a formal specification language | j flokstra , m keulen , j skowronek | sigmod conference |   1994 |

Entity B:
| title                                                                                | authors                                 | venue           |   year |
|:-------------------------------------------------------------------------------------|:----------------------------------------|:----------------|-------:|
| the impress ddt : a database design toolbox based on a formal specification language | j flokstra , m van keulen , j skowronek | sigmod record , |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1803
rtable_id: 21365
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 |
|:--------------------------------------------------|:-------------------------------|:--------|-------:|
| efficient algorithms for processing xpath queries | g gottlob , c koch , r pichler | vldb    |   2002 |

Entity B:
| title                                                          | authors                                    | venue   |   year |
|:---------------------------------------------------------------|:-------------------------------------------|:--------|-------:|
| xxl : a prototype for a library of query processing algorithms | j van den bercken , jp dittrich , b seeger |         |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2188
rtable_id: 13443
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 |
|:----------------------------------|:-----------------------------------|:------------------|-------:|
| global optimization of histograms | h jagadish , h jin , b ooi , k tan | sigmod conference |   2001 |

Entity B:
| title                                           | authors   | venue      | year   |
|:------------------------------------------------|:----------|:-----------|:-------|
| global optimization using interval analysis . m | er hansen | new york , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 608
rtable_id: 14394
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 |
|:---------------------------------------------|:----------|:--------|-------:|
| database management systems and the internet | s malaika | vldb    |   1996 |

Entity B:
| title                                                    | authors                                       | venue                                                          | year   |
|:---------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------|:-------|
| file management in the exodus extensible database system | m carey , d dewitt , j richardson , es object | proc . int . conf . on very large data bases , kyoto , japan ( |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1841
rtable_id: 15876
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 |
|:----------------------------|:-------------------------------|:------------------|-------:|
| xsb as a deductive database | k sagonas , t swift , d warren | sigmod conference |   1994 |

Entity B:
| title                                     | authors                                      | venue                                  |   year |
|:------------------------------------------|:---------------------------------------------|:---------------------------------------|-------:|
| space optimization in deductive databases | a baltrunas , d srivastava , s sudarshan , r | acm transactions on database systems , |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1855
rtable_id: 22756
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 |
|:-------------------------------------------------------|:---------------------------------------------------|:------------------|-------:|
| gigascope : a stream database for network applications | c cranor , t johnson , o spatscheck , v shkapenyuk | sigmod conference |   2003 |

Entity B:
| title                                      | authors               | venue                | year   |
|:-------------------------------------------|:----------------------|:---------------------|:-------|
| data streams : algorithms and applications | j mirkovic , p reiher | proc . of acm soda , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 304
rtable_id: 50617
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 unified framework for enforcing multiple access control policies | s jajodia , p samarati , v subrahmanian , e bertino | sigmod conference |   1997 |

Entity B:
| title                                                           | authors                            | venue   | year   |
|:----------------------------------------------------------------|:-----------------------------------|:--------|:-------|
| supporting multiple access control policies in database systems | e bertino , s jajodia , p samarati |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1314
rtable_id: 17835
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 |
|:-----------------------------------------------|:-----------|:--------|-------:|
| foundations of preferences in database systems | w kieпling | vldb    |   2002 |

Entity B:
| title                                                | authors                                   | venue   | year   |
|:-----------------------------------------------------|:------------------------------------------|:--------|:-------|
| optimization of sequence queries in database systems | r sadri , c zaniolo , a zarkesh , j adibi |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2220
rtable_id: 29569
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 |
|:---------------------------------------------------|:---------------------------------------|:--------|-------:|
| towards event-driven modelling for database design | m teisseire , p poncelet , r cicchetti | vldb    |   1994 |

Entity B:
| title                              | authors                                | venue   |   year |
|:-----------------------------------|:---------------------------------------|:--------|-------:|
| a tool for modular database design | l tucherman , al furtado , ma casanova | vldb ,  |   1985 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 147
rtable_id: 16310
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 |
|:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------|:--------|-------:|
| algorithms for mining association rules for binary segmentations of huge categorical databases | y morimoto , t fukuda , h matsuzawa , t tokuyama , k yoda | vldb    |   1998 |

Entity B:
| title                                                            | authors                            | venue                                           | year   |
|:-----------------------------------------------------------------|:-----------------------------------|:------------------------------------------------|:-------|
| mining association rules between sets ofitems in large databases | r agrawal , t imielinski , a swami | conference on management of data . washington , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1640
rtable_id: 55264
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 query language for multidimensional arrays : design , implementation , and optimization techniques | l libkin , r machlin , l wong | sigmod conference |   1996 |

Entity B:
| title                                                                                            | authors                    | venue                                                            | year   |
|:-------------------------------------------------------------------------------------------------|:---------------------------|:-----------------------------------------------------------------|:-------|
| design and implementation of nd-sql , a multi-dimensional language for interoperability and olap | f gingras , lvs lakshmanan | technical report , concordia university , montreal , canada , in |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1466
rtable_id: 60341
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 |
|:----------------------------------------------------------------|:----------------------|:--------|-------:|
| fast algorithms for mining association rules in large databases | r agrawal , r srikant | vldb    |   1994 |

Entity B:
| title                                                             | authors                      | venue                                                                             | year   |
|:------------------------------------------------------------------|:-----------------------------|:----------------------------------------------------------------------------------|:-------|
| mining association rules between sets of items in large databases | a rakesh , i tomasz , s arun | & hellip ; of the 1993 acm sigmod conference on management of data , & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 95
rtable_id: 26554
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 |
|:-----------------------------------|:------------------|:------------------|-------:|
| on-line warehouse view maintenance | d quass , j widom | sigmod conference |   1997 |

Entity B:
| title                                                        | authors                               | venue                          | year   |
|:-------------------------------------------------------------|:--------------------------------------|:-------------------------------|:-------|
| view maintenance in a warehouse environment , acm sigmod int | y zhuge , h garcia-molina , jl wiener | conf . on management of data , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1392
rtable_id: 62311
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 |
|:-------------------------------------------------------------------------|:----------------------|:--------|-------:|
| qualitative representation of spatial knowledge in two-dimensional space | d papadias , t sellis | vldb j. |   1994 |

Entity B:
| title                                              | authors    | venue                            |   year |
|:---------------------------------------------------|:-----------|:---------------------------------|-------:|
| representation of three-dimensional digital images | sn srihari | acm computing surveys ( csur ) , |   1981 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2542
rtable_id: 29169
label: 0
Task: Entity-Matching (source dataset: Amazon-Google)

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                                    | manufacturer         | price   |
|:-----------------------------------------|:---------------------|:--------|
| instant immersion spanish ( audio book ) | topics entertainment |         |

Entity B:
| title                         | manufacturer   |   price |
|:------------------------------|:---------------|--------:|
| instant immers spanish dlux 2 |                |   36.11 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 105
rtable_id: 3021
label: 0
Task: Entity-Matching (source dataset: Amazon-Google)

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                  | manufacturer   |   price |
|:-----------------------|:---------------|--------:|
| adobe premiere pro 2.0 | adobe          |     849 |

Entity B:
| title                    | manufacturer   |   price |
|:-------------------------|:---------------|--------:|
| adobe cs3 design premium |                | 1865.99 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 322
rtable_id: 521
label: 0
Task: Entity-Matching (source dataset: Amazon-Google)

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              | manufacturer   |   price |
|:-------------------|:---------------|--------:|
| adobe audition 2.0 | adobe          |     349 |

Entity B:
| title                                     | manufacturer   |   price |
|:------------------------------------------|:---------------|--------:|
| adobe 22011152 audition2 win retail ue 1u |                |   296.2 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 272
rtable_id: 2337
label: 1
Task: Entity-Matching (source dataset: Amazon-Google)

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                                 | manufacturer   |   price |
|:--------------------------------------|:---------------|--------:|
| norton internet security 2008 10 user | symantec       |  199.99 |

Entity B:
| title                        | manufacturer   |   price |
|:-----------------------------|:---------------|--------:|
| mcafee internet security 7.0 |                |   69.99 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1008
rtable_id: 1190
label: 0