Triple

T20801607
Position Surface form Disambiguated ID Type / Status
Subject Paide E512053 entity
Predicate locatedIn P40 FINISHED
Object Järva County NE NERFINISHED

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: Järva County | Statement: [Paide, locatedIn, Järva County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Järva County
Context triple: [Paide, locatedIn, Järva County]
  • A. Järva County chosen
    Järva County is a historical and administrative region in central Estonia known for its rural landscapes and small towns.
  • B. Viljandi County
    Viljandi County is a rural administrative region in southern Estonia known for its lakes, forests, and historic town of Viljandi.
  • C. Tartu County
    Tartu County is an administrative region in eastern Estonia centered around the university city of Tartu and known for its cultural, educational, and economic significance.
  • D. Lääne-Viru County
    Lääne-Viru County is a northeastern administrative region of Estonia known for its coastal landscapes, historic manors, and the town of Rakvere.
  • E. Võru County
    Võru County is a rural region in southeastern Estonia known for its distinct South Estonian (Võro) linguistic and cultural heritage.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4cc69f481908e98751e697b9df4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2b0d75881909cc89ebe4e27adc1 completed April 21, 2026, 12:20 a.m.
Created at: April 16, 2026, 12:39 p.m.