Triple

T18915017
Position Surface form Disambiguated ID Type / Status
Subject Tolga E462702 entity
Predicate hasNeighbour P5707 FINISHED
Object Tynset 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: Tynset | Statement: [Tolga, hasNeighbour, Tynset]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tynset
Context triple: [Tolga, hasNeighbour, Tynset]
  • A. Tynset chosen
    Tynset is a rural municipality in Innlandet county, Norway, known for its vast mountain landscapes, agriculture, and role as a regional service center in the Østerdalen valley.
  • B. Tyssedal
    Tyssedal is a small industrial village in Vestland county, Norway, known for its historic hydropower facilities and scenic location by the Sørfjorden.
  • C. Vangsnes
    Vangsnes is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and agricultural surroundings.
  • D. Nissedal
    Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
  • E. Trysil
    Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive outdoor recreation opportunities.
  • 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_69d8dcfdbbb881909964fa5a75bd0b48 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5c62685408190b17280147e1c247a completed April 20, 2026, 6:22 a.m.
Created at: April 10, 2026, 11:58 a.m.