Triple

T3525302
Position Surface form Disambiguated ID Type / Status
Subject Kaposvár E74524 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: [Kaposvár, locatedIn, Somogy County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Somogy County
Context triple: [Kaposvár, 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. Busko County
    Busko County is an administrative district in south-central Poland, known for its spa town Busko-Zdrój and its location within the Świętokrzyskie Voivodeship.
  • 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_69ad85d0c5488190a3d8e02ebd01a1aa completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc6a8d0c819094d38b9c47fb67b4 completed March 8, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e8de2648190809369c3f0b7d85d completed March 13, 2026, 3:03 a.m.
Created at: March 8, 2026, 3:19 p.m.