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 |
|:-----------------------------------------------------------------------|:--------------------------------------|:--------|-------:|
| an efficient algorithm for mining association rules in large databases | a savasere , e omiecinski , s navathe | vldb    |   1995 |

Entity B:
| title                                                             | authors               | venue                                                                 | year   |
|:------------------------------------------------------------------|:----------------------|:----------------------------------------------------------------------|:-------|
| fast algorithms for mining association rules in large datab as es | r agrawal , r srikant | proceedings of the 20th international conference on very & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1953
rtable_id: 45920
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 |
|:--------------------------------------------|:----------|:--------|-------:|
| an introduction to spatial database systems | r gќting  | vldb j. |   1994 |

Entity B:
| title                                                              | authors   | venue                    |   year |
|:-------------------------------------------------------------------|:----------|:-------------------------|-------:|
| an introduction to real-time operating systems : scheduling theory | cw mercer | unpublished manuscript , |   1992 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2538
rtable_id: 7674
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 |
|:----------------|:----------|:--------|-------:|
| guest editorial |           |         |   2003 |

Entity B:
| title     | authors                 | venue                   |   year |
|:----------|:------------------------|:------------------------|-------:|
| editorial | pj sheppard , gr walker | bt technology journal , |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2280
rtable_id: 42797
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 |
|:----------------------------------------------------------------------------------------------|:-----------------------------|:--------|-------:|
| oasis : an online and accurate technique for local-alignment searches on biological sequences | c meek , j patel , s kasetty | vldb    |   2003 |

Entity B:
| title                                                                                         | authors                      | venue                                                        | year   |
|:----------------------------------------------------------------------------------------------|:-----------------------------|:-------------------------------------------------------------|:-------|
| oasis : an online and accurate technique for local-alignment searches on biological databases | c meek , j patel , s kasetty | proceedings of the conference on very large databases ( vldb |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2025
rtable_id: 14305
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 |
|:----------------------------------------------------|:------------------------------|:----------------------------|-------:|
| synthesis of extended transaction models using acta | p chrysanthis , k ramamritham | acm trans . database syst . |   1994 |

Entity B:
| title                                                                 | authors         | venue   |   year |
|:----------------------------------------------------------------------|:----------------|:--------|-------:|
| a practical and modular implementation of extended transaction models | rs barga , c pu | vldb ,  |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2307
rtable_id: 61056
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 |
|:-----------------------------------------------------------------|:----------------------------------------|:--------|-------:|
| distributed processing over stand-alone systems and applications | g alonso , c hagen , h schek , m tresch | vldb    |   1997 |

Entity B:
| title                                          | authors                                | venue   | year   |
|:-----------------------------------------------|:---------------------------------------|:--------|:-------|
| using events to build distributed applications | j bacon , j bates , r hayton , k moody |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1721
rtable_id: 31483
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 network is the database : data management for highly distributed systems | j navas , m wynblatt | sigmod conference |   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: 707
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 |
|:-----------------------------|:----------------------------|:------------------|-------:|
| data grid management systems | a jagatheesan , a rajasekar | sigmod conference |   2003 |

Entity B:
| title                                                                | authors                            | venue   | year   |
|:---------------------------------------------------------------------|:-----------------------------------|:--------|:-------|
| definition and evaluation of access rules in data management systems | eb fernandez , rc summers , t lang | vldb ,  |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 288
rtable_id: 3992
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 |
|:---------------------------------------|:--------------------|:------------------|-------:|
| query processing techniques for arrays | a marathe , k salem | sigmod conference |   1999 |

Entity B:
| title                                              | authors       | venue                                                              | year   |
|:---------------------------------------------------|:--------------|:-------------------------------------------------------------------|:-------|
| proof techniques for resource controller processes | k ramamritham | coins technical report , university of massachusetts , june 1982 , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1060
rtable_id: 45746
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 |
|:--------------------------------------------------------------------------------------------|:---------------------------------------|:------------------|-------:|
| maintaining database consistency in presence of value dependencies in multidatabase systems | c morpain , m cart , j ferriщ , j pons | sigmod conference |   1996 |

Entity B:
| title                                                                         | authors                                            | venue   |   year |
|:------------------------------------------------------------------------------|:---------------------------------------------------|:--------|-------:|
| maintaining database consistency in heterogeneous distributed databasesystems | s mehrotra , r rastogi , hf korth , a silberschatz |         |   1991 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1508
rtable_id: 47512
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 |
|:-------------------------------------------------------------------------------|:------------------------------------|:--------|-------:|
| rainforest - a framework for fast decision tree construction of large datasets | j gehrke , r ramakrishnan , v ganti | vldb    |   1998 |

Entity B:
| title                                                                            | authors                             | venue                                 |   year |
|:---------------------------------------------------------------------------------|:------------------------------------|:--------------------------------------|-------:|
| rainforestâ ?? a framework for fast decision tree construction of large datasets | j gehrke , r ramakrishnan , v ganti | data mining and knowledge discovery , |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1738
rtable_id: 7894
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 |
|:-------------------------------------------------------------------------------------------|:------------------------------------------------|:------------------|-------:|
| cmvf : a novel dimension reduction scheme for efficient indexing in a large image database | j shen , a ngu , j shepherd , d huynh , q sheng | sigmod conference |   2003 |

Entity B:
| title                                                                              | authors                                          | venue               |   year |
|:-----------------------------------------------------------------------------------|:-------------------------------------------------|:--------------------|-------:|
| locally adaptive dimensionality reduction for indexing large time series databases | e keogh , k chakrabarti , s mehrotra , m pazzani | sigmod conference , |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 297
rtable_id: 10963
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 |
|:-----------------|:----------|:--------|-------:|
| chair 's message |           |         |   2002 |

Entity B:
| title               | authors                                       | venue   | year   |
|:--------------------|:----------------------------------------------|:--------|:-------|
| director 's message | ofe announcements , webb learning , f commons |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2585
rtable_id: 44226
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 |
|:------------------------------------------------------|:----------------------|:------------------|-------:|
| mining frequent patterns without candidate generation | j han , j pei , y yin | sigmod conference |   2000 |

Entity B:
| title                                                | authors               | venue                                                                     | year   |
|:-----------------------------------------------------|:----------------------|:--------------------------------------------------------------------------|:-------|
| mining frequent pattems without candidate generation | j han , j pei , y yin | prtoceedings of the 2000 acm sigmod international conference & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 810
rtable_id: 31345
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 |
|:--------------------------------------------------------------|:------------------------------|:--------|-------:|
| managing intervals efficiently in object-relational databases | h kriegel , m pіtke , t seidl | vldb    |   2000 |

Entity B:
| title                                                                       | authors                 | venue           |   year |
|:----------------------------------------------------------------------------|:------------------------|:----------------|-------:|
| extraction of object-oriented structures from existing relational databases | s ramanathan , j hodges | sigmod record , |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1350
rtable_id: 18926
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 |
|:---------------------------|:-------------------|:--------|-------:|
| query optimization for xml | j mchugh , j widom | vldb    |   1999 |

Entity B:
| title                                             | authors                                    | venue                                                                | year   |
|:--------------------------------------------------|:-------------------------------------------|:---------------------------------------------------------------------|:-------|
| efficient algorithms for multi query optimization | p roy , s seshadri , s sudarshan , s bhobe | proceedings of the 2000 acm international conference on & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1431
rtable_id: 60919
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 views with duplicates | t griffin , l libkin | sigmod conference |   1995 |

Entity B:
| title                                                                                     | authors              | venue        | year   |
|:------------------------------------------------------------------------------------------|:---------------------|:-------------|:-------|
| ã cents â ? ¬ å?incremental maintenance of views with duplicates , ã cents â ? ¬ â ? proc | t griffin , l libkin | acm sigmod , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1618
rtable_id: 38950
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 |
|:-----------------------------|:---------------------|:--------|-------:|
| relating distributed objects | b martin , r cattell | vldb    |   1994 |

Entity B:
| title                           | authors                       | venue   | year   |
|:--------------------------------|:------------------------------|:--------|:-------|
| configuring distributed systems | j kramer , j magee , m sloman |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 201
rtable_id: 46691
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 performance evaluation of oid mapping techniques | a eickler , c gerlhof , d kossmann | vldb    |   1995 |

Entity B:
| title                                            | authors                  | venue                                                                  |   year |
|:-------------------------------------------------|:-------------------------|:-----------------------------------------------------------------------|-------:|
| an evaluation of generic bulk loading techniques | j den bercken , b seeger | proceedings of the international conference on very large & hellip ; , |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1921
rtable_id: 20209
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 |
|:------------------------------------------------------------------------------------------|:----------------------------------|:--------|-------:|
| unql : a query language and algebra for semistructured data based on structural recursion | p buneman , m fernandez , d suciu | vldb j. |   2000 |

Entity B:
| title                                                                                     | authors                           | venue                                                                 |   year |
|:------------------------------------------------------------------------------------------|:----------------------------------|:----------------------------------------------------------------------|-------:|
| unql : a query language and algebra for semistructured data based on structural recursion | p buneman , m fernandez , d suciu | the vldb journal the international journal on very large & hellip ; , |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2394
rtable_id: 34523
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 |
|:---------------------------------------------------|:------------------------------------------------------------------------------------|:--------|-------:|
| mariposa : a wide-area distributed database system | m stonebraker , p aoki , w litwin , a pfeffer , a sah , j sidell , c staelin , a yu | vldb j. |   1996 |

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



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2528
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 |
|:-------------------------------------------------|:---------------------|:------------------|-------:|
| incremental maintenance of views with duplicates | t griffin , l libkin | sigmod conference |   1995 |

Entity B:
| title                                                          | authors   | venue   | year   |
|:---------------------------------------------------------------|:----------|:--------|:-------|
| incremental computation and maintenance of temporal aggregates | y jun     |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1618
rtable_id: 23389
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 |
|:-------------------------------------------|:------------|:--------------|-------:|
| repositories and object oriented databases | p bernstein | sigmod record |   1998 |

Entity B:
| title                                                             | authors                 | venue                  |   year |
|:------------------------------------------------------------------|:------------------------|:-----------------------|-------:|
| an overview of vertical partitioning in object oriented databases | gs chinchwadkar , a goh | the computer journal , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 632
rtable_id: 40726
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 estimation of user-defined methods in object-relational database systems | j boulos , k ono | sigmod record |   1999 |

Entity B:
| title                                                | authors            | venue                          |   year |
|:-----------------------------------------------------|:-------------------|:-------------------------------|-------:|
| schema evolution in object-oriented database systems | gt nguyen , d rieu | data & knowledge engineering , |   1989 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 488
rtable_id: 22975
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 x-tree : an index structure for high-dimensional data | s berchtold , d keim , h kriegel | vldb    |   1996 |

Entity B:
| title                                                              | authors                                | venue   | year   |
|:-------------------------------------------------------------------|:---------------------------------------|:--------|:-------|
| an adaptive index structure for high-dimensional similarity search | p wu , bs manjunath , s chandrasekaran |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1834
rtable_id: 62603
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 |
|:--------------------------------------------|:----------|:------------------|-------:|
| belief reasoning in mls deductive databases | h jamil   | sigmod conference |   1999 |

Entity B:
| title                                               | authors                    | venue                                  |   year |
|:----------------------------------------------------|:---------------------------|:---------------------------------------|-------:|
| reactive consistency control in deductive databases | g moerkotte , pc lockemann | acm transactions on database systems , |   1991 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 953
rtable_id: 52025
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 |
|:---------------------------------|:-----------------------------------------|:------------------|-------:|
| range queries in olap data cubes | c ho , r agrawal , n megiddo , r srikant | sigmod conference |   1997 |

Entity B:
| title                                                    | authors                                   | venue                                                                  | year   |
|:---------------------------------------------------------|:------------------------------------------|:-----------------------------------------------------------------------|:-------|
| range queries in olap data cubes , in â ?? sigmodâ ?? 97 | ct ho , r agrawal , n megiddo , r srikant | & hellip ; acm sigmod international conference on management of data , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1302
rtable_id: 2013
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 |
|:-------------------------------------------------------------------------------------------|:---------------------------|:------------------|-------:|
| parallel mining algorithms for generalized association rules with classification hierarchy | t shintani , m kitsuregawa | sigmod conference |   1998 |

Entity B:
| title                                                                                              | authors   | venue   | year   |
|:---------------------------------------------------------------------------------------------------|:----------|:--------|:-------|
| fast sequential and parallel algorithms for association rule mining : a comparison , university of | a mueller | md ,    |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1213
rtable_id: 26033
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 |
|:------------------------|:------------------------------|:------------------|-------:|
| webview materialization | a labrinidis , n roussopoulos | sigmod conference |   2000 |

Entity B:
| title                        | authors                | venue   | year   |
|:-----------------------------|:-----------------------|:--------|:-------|
| materialized views in action | aq primer , aed schema |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 841
rtable_id: 49711
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 high-dimensional data search in incomplete databases | b ooi , c goh , k tan | vldb    |   1998 |

Entity B:
| title                                                   | authors                      | venue                                                                  |   year |
|:--------------------------------------------------------|:-----------------------------|:-----------------------------------------------------------------------|-------:|
| incomplete information in relational temporal databases | sk gadia , ss nair , yc poon | proceedings of the international conference on very large & hellip ; , |   1992 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1639
rtable_id: 16650
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 |
|:------------------------------------------|:----------------------|:--------|-------:|
| query engines for web-accessible xml data | l fegaras , r elmasri | vldb    |   2001 |

Entity B:
| title                                      | authors                       | venue                                                                 |   year |
|:-------------------------------------------|:------------------------------|:----------------------------------------------------------------------|-------:|
| an xml query engine for network-bound data | zg ives , ay halevy , ds weld | the vldb journal the international journal on very large & hellip ; , |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1050
rtable_id: 50805
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 systems management and oracle8 | c doherty | sigmod conference |   1998 |

Entity B:
| title                                               | authors                            | venue                                  |   year |
|:----------------------------------------------------|:-----------------------------------|:---------------------------------------|-------:|
| a database management system for the federal courts | jr buchanan , rd fennell , h samet | acm transactions on database systems , |   1984 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1105
rtable_id: 11056
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 |
|:--------------------------------------------------------|:----------------------|:--------------|-------:|
| wrapper generation for semi-structured internet sources | n ashish , c knoblock | sigmod record |   1997 |

Entity B:
| title                                                                           | authors   | venue                                                                  | year   |
|:--------------------------------------------------------------------------------|:----------|:-----------------------------------------------------------------------|:-------|
| craig knoblock-wrapper generation for semi-structured internet sourcesâ ?? proc | n ashish  | workshop on management of semi-structured data , tucson , & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 524
rtable_id: 51493
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 taxonomy for secure object-oriented databases | m olivier , s solms | acm trans . database syst . |   1994 |

Entity B:
| title                                     | authors   | venue   | year   |
|:------------------------------------------|:----------|:--------|:-------|
| introduction to object-oriented databases | w kim     |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2300
rtable_id: 7564
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 |
|:------------------------------------------------------------|:-------------------------------------------------------|:--------------|-------:|
| lore : a database management system for semistructured data | j mchugh , s abiteboul , r goldman , d quass , j widom | sigmod record |   1997 |

Entity B:
| title                                                         | authors                    | venue                           | year   |
|:--------------------------------------------------------------|:---------------------------|:--------------------------------|:-------|
| dynamat : a dynamic view management system for data warehouse | y kotodis , n roussopoulos | proceedings of the acm sigmod , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 570
rtable_id: 16348
label: 0