Triple

T5969210
Position Surface form Disambiguated ID Type / Status
Subject Siófok E132830 entity
Predicate locatedIn P40 FINISHED
Object Somogy County E234838 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: Somogy County | Statement: [Siófok, locatedIn, Somogy County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Somogy County
Context triple: [Siófok, locatedIn, Somogy County]
  • A. Somogy County chosen
    Somogy County is an administrative region in southwestern Hungary, known for its rural landscapes and proximity to Lake Balaton.
  • B. Fejér County
    Fejér County is an administrative region in central Hungary known for its historical significance and industrial centers, with Székesfehérvár as its county seat.
  • C. Tolna County
    Tolna County is an administrative region in central Hungary known for its agricultural landscape and location along the Danube River.
  • D. Nógrád County
    Nógrád County is a northern Hungarian administrative region known for its hilly landscapes, historic towns, and portions of the Mátra and Cserhát mountain ranges.
  • E. Liptó County
    Liptó County was a historic administrative county of the Kingdom of Hungary, located in the northern part of present-day Slovakia and centered around the Liptov region.
  • 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_69c0086deab081908550159ca23eec9b completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c03a40cfe08190a40de42831af7cf8 completed March 22, 2026, 6:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0e40506848190843971e772d56054 completed March 23, 2026, 6:56 a.m.
Created at: March 22, 2026, 4:03 p.m.