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

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

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

Entity A:
| title           | authors    | venue         |   year |
|:----------------|:-----------|:--------------|-------:|
| editor 's notes | m franklin | sigmod record |   1997 |

Entity B:
| title           | authors                        | venue                                      | year   |
|:----------------|:-------------------------------|:-------------------------------------------|:-------|
| editors ' notes | n van note chism , dj bickford | new directions for teaching and learning , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 534
rtable_id: 50551
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 |
|:----------------------------------------------------|:-------------------|:--------|-------:|
| quickstore : a high performance mapped object store | s white , d dewitt | vldb j. |   1995 |

Entity B:
| title                                  | authors               | venue                                                      | year   |
|:---------------------------------------|:----------------------|:-----------------------------------------------------------|:-------|
| a high performance mapped object store | sj white , dos dewitt | proceedings of the acm sigmod conference on the management |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2522
rtable_id: 12433
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 |
|:-------------------------------------------------|:--------------------|:--------------|-------:|
| comparative analysis of five xml query languages | a bonifati , s ceri | sigmod record |   2000 |

Entity B:
| title                                      | authors             | venue           | year   |
|:-------------------------------------------|:--------------------|:----------------|:-------|
| comparative analysis of five xml languages | a bonifati , s ceri | sigmod record , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 282
rtable_id: 20396
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 general techniques for querying xml documents using a relational database system | j shanmugasundaram , e shekita , j kiernan , r krishnamurthy , s viglas , j naughton , i tatarinov | sigmod record |   2001 |

Entity B:
| title                                                                | authors                                           | venue                                                                 | year   |
|:---------------------------------------------------------------------|:--------------------------------------------------|:----------------------------------------------------------------------|:-------|
| relational databases for xml documents : limitationsandopportunities | j shanmugasundaram , k tufte , c zhang , g he , d | proc . oftheconferenceonvery large data bases ( vldb ) , & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 182
rtable_id: 47311
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 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 structural approach for the definition of semantics of active database | p fraernali , l tanca | acm tods , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2263
rtable_id: 1848
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                          | authors             | venue             |   year |
|:-------------------------------|:--------------------|:------------------|-------:|
| the ecrc multi database system | w jonker , h schќtz | sigmod conference |   1995 |

Entity B:
| title                                   | authors                                              | venue                                     |   year |
|:----------------------------------------|:-----------------------------------------------------|:------------------------------------------|-------:|
| the nu-prolog deductive database system | k ramamohanarao , j shepherd , i balbin , g port , l | halstead artificial intelligence series , |   1989 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1712
rtable_id: 12356
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 |
|:--------------------------------|:---------------------------|:--------|-------:|
| semantic caching of web queries | b chidlovskii , u borghoff | vldb j. |   2000 |

Entity B:
| title                                           | authors                                    | venue   | year   |
|:------------------------------------------------|:-------------------------------------------|:--------|:-------|
| optimizing web queries through semantic caching | b chidlovskii , c roncancio , ml schneider |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2392
rtable_id: 29708
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 |
|:----------------|:------------|:--------------------------------------|-------:|
| guest editorial | s chaudhuri | data mining and knowledge discovery , |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2280
rtable_id: 18281
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 |
|:----------------------------------------------------------------------------------------------------------------------------|:----------|:--------|-------:|
| proceedings of the 2000 acm sigmod international conference on management of data , may 16-18 , 2000 , dallas , texas , usa |           |         |   2000 |

Entity B:
| title                                                         | authors    | venue                                                       |   year |
|:--------------------------------------------------------------|:-----------|:------------------------------------------------------------|-------:|
| source international conference on management of data archive | j clifford | proceedings of the 1991 acm sigmod international conference |   1991 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 804
rtable_id: 38608
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 |
|:----------------------------------------------------|:-----------------------------|:------------------|-------:|
| wavelet-based histograms for selectivity estimation | y matias , j vitter , m wang | sigmod conference |   1998 |

Entity B:
| title                                                                           | authors                    | venue               |   year |
|:--------------------------------------------------------------------------------|:---------------------------|:--------------------|-------:|
| multi-dimensional selectivity estimation using compressed histogram information | jh lee , dh kim , cw chung | sigmod conference , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1172
rtable_id: 2004
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 fuzzy association rules in databases | c kuok , a fu , m wong | sigmod record |   1998 |

Entity B:
| title                                               | authors                | venue                                                 | year   |
|:----------------------------------------------------|:-----------------------|:------------------------------------------------------|:-------|
| scalable parallel data mining for association rules | h eui-hong , g karypis | ieee transactions on knowledge and data engineering , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 612
rtable_id: 45648
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 |
|:-----------------------------------------------------------------------|:------------------------------------------------------|:------------------|-------:|
| benchmarking queries over trees : learning the hard truth the hard way | f wattez , s cluet , v benzaken , g ferran , c fiegel | sigmod conference |   2000 |

Entity B:
| title                                                                  | authors                                               | venue               |   year |
|:-----------------------------------------------------------------------|:------------------------------------------------------|:--------------------|-------:|
| benchmarking queries over trees : learning the hard truth the hard way | f wattez , s cluet , v benzaken , g ferran , c fiegel | acm sigmod record , |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 782
rtable_id: 50143
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 |
|:---------------------------------------------------------------------------|:-----------------------------------------------|:------------------|-------:|
| materialized view selection and maintenance using multi-query optimization | h mistry , p roy , s sudarshan , k ramamritham | sigmod conference |   2001 |

Entity B:
| title                                                              | authors   | venue   | year   |
|:-------------------------------------------------------------------|:----------|:--------|:-------|
| rainbowii : multi-xquery optimization using materialized xml views | x zhang   |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 669
rtable_id: 14348
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 |
|:--------------------------------------------------------------|:---------------------------------|:------------------|-------:|
| efficient algorithms for mining outliers from large data sets | s ramaswamy , r rastogi , k shim | sigmod conference |   2000 |

Entity B:
| title                                             | authors                        | venue                                  |   year |
|:--------------------------------------------------|:-------------------------------|:---------------------------------------|-------:|
| efficient algorithms for processing xpath queries | g gottlob , c koch , r pichler | acm transactions on database systems , |   2005 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 772
rtable_id: 7012
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 |
|:----------------|:---------------|:--------------|-------:|
| editor 's notes | jennifer widom | sigmod record |   1996 |

Entity B:
| title           | authors                        | venue                                      | year   |
|:----------------|:-------------------------------|:-------------------------------------------|:-------|
| editors ' notes | n van note chism , dj bickford | new directions for teaching and learning , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 741
rtable_id: 50551
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 parallel execution of multiple pipelined hash joins | h hsiao , m chen , p yu | sigmod conference |   1994 |

Entity B:
| title                                                                      | authors                            | venue                                                                  |   year |
|:---------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------------------------------|-------:|
| using segmented right-deep trees for the execution of pipelined hash joins | ms chen , ml lo , ps yu , hc young | proceedings of the international conference on very large & hellip ; , |   1992 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1771
rtable_id: 5273
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 design and performance evaluation of alternative xml storage strategies | f tian , d dewitt , j chen , c zhang | sigmod record |   2002 |

Entity B:
| title                                                                       | authors                               | venue   |   year |
|:----------------------------------------------------------------------------|:--------------------------------------|:--------|-------:|
| the design and performance evaluation of altervative xml storage strategies | f tian , dj dewitt , j chen , c zhang |         |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 227
rtable_id: 40509
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 |
|:-------------------------------------|:-----------------|:--------------|-------:|
| continuous queries over data streams | s babu , j widom | sigmod record |   2001 |

Entity B:
| title                                 | authors                       | venue         | year   |
|:--------------------------------------|:------------------------------|:--------------|:-------|
| streaming data over streaming queries | s chandrasekaran , m franklin | submitted for |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 178
rtable_id: 48824
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                                                 | authors                    | venue   |   year |
|:------------------------------------------------------|:---------------------------|:--------|-------:|
| distance-based outliers : algorithms and applications | e knorr , r ng , v tucakov | vldb j. |   2000 |

Entity B:
| title                                                                                             | authors     | venue                   | year   |
|:--------------------------------------------------------------------------------------------------|:------------|:------------------------|:-------|
| re-designing distance functions and distance-based applications for high dimensional applications | cc aggarwal | proc . of sigmod-pods , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2405
rtable_id: 23228
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 patchwork on the internet | r braumandl , a kemper , d kossmann | sigmod conference |   1999 |

Entity B:
| title                                                           | authors                             | venue                                                   | year   |
|:----------------------------------------------------------------|:------------------------------------|:--------------------------------------------------------|:-------|
| database patchwork on the internet ( project demo description ) | r braumandl , a kemper , d kossmann | proc . of the acm sigmod conf . on management of data , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 971
rtable_id: 17079
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 |
|:--------------------------------------------------|:---------------------------------|:--------|-------:|
| discover : keyword search in relational databases | v hristidis , y papakonstantinou | vldb    |   2002 |

Entity B:
| title                                                                           | authors        | venue   |   year |
|:--------------------------------------------------------------------------------|:---------------|:--------|-------:|
| indexing relational database content offline for efficient keyword-based search | q su , j widom |         |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2125
rtable_id: 64083
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 |
|:---------------------------------------------------------|:----------|:------------------|-------:|
| efficient algorithms for minimizing tree pattern queries | p ramanan | sigmod conference |   2002 |

Entity B:
| title                                                       | authors     | venue                                                                  | year   |
|:------------------------------------------------------------|:------------|:-----------------------------------------------------------------------|:-------|
| spade : an efficient algorithm for mining frequent sequence | jz mohammed | & hellip ; learning journal , special issue on unsupervised learning , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 506
rtable_id: 49375
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 |
|:-------------------------------------------|:-------------------|:--------------|-------:|
| asserting beliefs in mls relational models | n jukic , s vrbsky | sigmod record |   1997 |

Entity B:
| title                                    | authors                                      | venue   | year   |
|:-----------------------------------------|:---------------------------------------------|:--------|:-------|
| learning probabilistic relational models | n friedman , l getoor , d koller , a pfeffer |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 558
rtable_id: 59728
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 design and implementation of a sequence database system | p seshadri , m livny , r ramakrishnan | vldb    |   1996 |

Entity B:
| title                                                    | authors              | venue             | year   |
|:---------------------------------------------------------|:---------------------|:------------------|:-------|
| the architecture of an active database management system | u dayal , d mccarthy | acm sigmod conf , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1826
rtable_id: 9565
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 |
|:-------------------------------------------------------|:------------------------------|:----------------------------|-------:|
| formal query languages for secure relational databases | m winslett , k smith , x qian | acm trans . database syst . |   1994 |

Entity B:
| title                                             | authors             | venue                                                                     | year   |
|:--------------------------------------------------|:--------------------|:--------------------------------------------------------------------------|:-------|
| on an algebra for historical relational databases | j cliord , a tansel | proceedings of the international conference on management of & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2310
rtable_id: 46920
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 |
|:----------------------------------------|:----------|:--------|-------:|
| algorithms for loading object databases | jl wiener |         |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 95
rtable_id: 26305
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 dynamically-fetched external information into a dbms for semistructured data | j mchugh , j widom | sigmod record |   1997 |

Entity B:
| title                                                  | authors                                           | venue                                                   | year   |
|:-------------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------------|:-------|
| ozone : integrating structured and semistructured data | s abiteboul , r goldman , t lahiri , j mchugh , j | technical report , stanford university database group , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 513
rtable_id: 8027
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                                        | authors                                                                             | venue             |   year |
|:---------------------------------------------|:------------------------------------------------------------------------------------|:------------------|-------:|
| the ores temporal database management system | b theodoulidis , a ait-braham , g andrianopoulos , j chaudhary , g karvelis , s sou | sigmod conference |   1994 |

Entity B:
| title                                                       | authors                                                | venue          | year   |
|:------------------------------------------------------------|:-------------------------------------------------------|:---------------|:-------|
| t1gukat : a uniform behavioral objectbase management system | mt ozsu , r peters , d szafron , b irani , a lipka , a | vldb journal , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1840
rtable_id: 19667
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 |
|:-----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| shoring up persistent applications | m carey , d dewitt , m franklin , n hall , m mcauliffe , j naughton , d schuh , m solomon , c tan , o tsatalos , s white , m zwilling | sigmod conference |   1994 |

Entity B:
| title                  | authors   | venue   |   year |
|:-----------------------|:----------|:--------|-------:|
| debugging applications | j robbins |         |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1780
rtable_id: 56286
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 |
|:---------------------------------------------------------------|:---------------------------|:------------------|-------:|
| dynamat : a dynamic view management system for data warehouses | y kotidis , n roussopoulos | sigmod conference |   1999 |

Entity B:
| title                                                        | authors   | venue               |   year |
|:-------------------------------------------------------------|:----------|:--------------------|-------:|
| tam : a system for dynamic transactional activity management | t zhou    | sigmod conference , |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 944
rtable_id: 28620
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 |
|:---------------------------------|:-------------------------------|:--------|-------:|
| coalescing in temporal databases | m bіhlen , r snodgrass , m soo | vldb    |   1996 |

Entity B:
| title             | authors          | venue               | year   |
|:------------------|:-----------------|:--------------------|:-------|
| temporal database | emk bibliography | acm sigmod record , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1811
rtable_id: 38046
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 |
|:---------------------------------------------------------------------------------|:----------------------|:--------|-------:|
| designing wrapper components for e-services in integrating heterogeneous systems | m mecella , b pernici | vldb j. |   2001 |

Entity B:
| title                                                                            | authors               | venue                                                                 |   year |
|:---------------------------------------------------------------------------------|:----------------------|:----------------------------------------------------------------------|-------:|
| designing wrapper components for e-services in integrating heterogeneous systems | m mecella , b pernici | the vldb journal the international journal on very large & hellip ; , |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2496
rtable_id: 49288
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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

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

Entity A:
| title                                  | authors                                               | venue             |   year |
|:---------------------------------------|:------------------------------------------------------|:------------------|-------:|
| the araneus web-base management system | g mecca , p atzeni , a masci , p merialdo , g sindoni | sigmod conference |   1998 |

Entity B:
| title                                    | authors                    | venue   | year   |
|:-----------------------------------------|:---------------------------|:--------|:-------|
| strudel : a web-site management system . | acms anthology , p edition |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1264
rtable_id: 57675
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 |
|:----------------------------------------|:----------------------|:--------------|-------:|
| managing uncertainty in sensor database | r cheng , s prabhakar | sigmod record |   2003 |

Entity B:
| title                                            | authors                                               | venue                                  |   year |
|:-------------------------------------------------|:------------------------------------------------------|:---------------------------------------|-------:|
| managing uncertainty in moving objects databases | g trajcevski , o wolfson , k hinrichs , s chamberlain | acm transactions on database systems , |   2004 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1885
rtable_id: 48831
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 |
|:-----------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| aurora : a data stream management system | d abadi , d carney , u чetintemel , m cherniack , c convey , c erwin , e galvez , m hatoun , a maskey , a rasin , a singer , m stonebraker , n tatbul , y xing , r yan , s zdonik | sigmod conference |   2003 |

Entity B:
| title             | authors       | venue   | year   |
|:------------------|:--------------|:--------|:-------|
| management system | csd recording |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 280
rtable_id: 45703
label: 0