Triple

T15302843
Position Surface form Disambiguated ID Type / Status
Subject Hardanger E365829 entity
Predicate hasPart P35 FINISHED
Object Kvam E365823 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: Kvam | Statement: [Hardanger, hasPart, Kvam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kvam
Context triple: [Hardanger, hasPart, Kvam]
  • A. Kvam chosen
    Kvam is a municipality in Vestland county, western Norway, known for its scenic location along the Hardangerfjord and traditional fruit farming.
  • B. Kvikne
    Kvikne is a rural village area in central Norway, known historically for mining and as the birthplace of Nobel Prize–winning writer Bjørnstjerne Bjørnson.
  • C. Jondal
    Jondal is a small village and former municipality in Vestland county, Norway, known as a gateway to the Folgefonna glacier and surrounding fjord landscape.
  • D. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • E. Laukvik
    Laukvik is a small coastal village in northern Norway, located in the Lofoten archipelago and known for its scenic Arctic landscapes and fishing heritage.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03ccd575c8190aa43262d3b73ef3c completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69feef86b8cc81909969098b1766f6b7 completed May 9, 2026, 8:25 a.m.
Created at: April 10, 2026, 3:15 a.m.