Triple

T5402406
Position Surface form Disambiguated ID Type / Status
Subject Komárom-Esztergom County E120808 entity
Predicate contains P35 FINISHED
Object Tatabánya E161753 NE FINISHED

How this triple was built (2 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: Tatabánya | Statement: [Komárom-Esztergom County, contains, Tatabánya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tatabánya
Context triple: [Komárom-Esztergom County, contains, Tatabánya]
  • A. Tatabánya chosen
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • 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. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • D. 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.
  • E. Bácska
    Bácska is a historical region in the Pannonian Plain, today divided between northern Serbia and southern Hungary, known for its multicultural population and agricultural importance.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd46391c0c81909fa484446732b6a3 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd87731c1c81909a4dc865282bd289 completed March 20, 2026, 5:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf77af7f0881908fad92962a2a6191 completed March 22, 2026, 5:01 a.m.
Created at: March 20, 2026, 2:04 p.m.