Triple

T9685866
Position Surface form Disambiguated ID Type / Status
Subject Northern Great Plain E234405 entity
Predicate containsCity P294 FINISHED
Object Jászberény E817188 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: Jászberény | Statement: [Northern Great Plain, containsCity, Jászberény]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jászberény
Context triple: [Northern Great Plain, containsCity, Jászberény]
  • A. Jászberény chosen
    Jászberény is a historic town in central Hungary known as a regional cultural and economic center of the Jászság area.
  • B. Bodrogköz
    Bodrogköz is a low-lying, marshy region in northeastern Hungary known for its riverine landscapes, wetlands, and traditional rural settlements.
  • C. Mezőberény
    Mezőberény is a town in southeastern Hungary known for its multicultural heritage and agricultural surroundings.
  • D. Bicske
    Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
  • E. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • 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_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_69d1bcbbe0108190a7011d52ba24ba4b completed April 5, 2026, 1:37 a.m.
Created at: March 30, 2026, 8:16 p.m.