Triple

T9685863
Position Surface form Disambiguated ID Type / Status
Subject Northern Great Plain E234405 entity
Predicate containsCity P294 FINISHED
Object Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
 E824580 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mátészalka | Statement: [Northern Great Plain, containsCity, Mátészalka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mátészalka
Context triple: [Northern Great Plain, containsCity, Mátészalka]
  • A. Mátraháza
    Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
  • B. Törökbálint
    Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
  • C. Nagyvázsony
    Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
  • D. Csákvár
    Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
  • E. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mátészalka
Triple: [Northern Great Plain, containsCity, Mátészalka]
Generated description
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mátészalka
Target entity description: Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
  • A. Mátraháza
    Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
  • B. Törökbálint
    Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
  • C. Nagyvázsony
    Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
  • D. Csákvár
    Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
  • E. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca84ca73208190957a900c8543bdcc completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9cd2dab481908e0d3fed28de9d40 completed April 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1d59ab0b48190acc133cc806582cc completed April 5, 2026, 3:23 a.m.
NEDg Description generation batch_69d1d656972881908ca41f7feb0f29c3 completed April 5, 2026, 3:26 a.m.
NED2 Entity disambiguation (via description) batch_69d1d6dc0bd8819082a5ad417ca87a76 completed April 5, 2026, 3:28 a.m.
Created at: March 30, 2026, 8:16 p.m.