Triple

T5837594
Position Surface form Disambiguated ID Type / Status
Subject Gassco E129509 entity
Predicate headquartersLocation P62 FINISHED
Object Karmøy E77362 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: Karmøy | Statement: [Gassco, headquartersLocation, Karmøy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karmøy
Context triple: [Gassco, headquartersLocation, Karmøy]
  • A. Karmøy chosen
    Karmøy is a large island and municipality in Rogaland county, Norway, known for its coastal fishing communities, maritime heritage, and historic Viking sites.
  • B. Rennesøy
    Rennesøy is an island and former municipality in Rogaland county, southwestern Norway, known for its coastal landscape and proximity to the city of Stavanger.
  • C. Skjervøy
    Skjervøy is a coastal fishing town and island community in northern Norway, known for its Arctic scenery and rich marine life.
  • D. Tromøya
    Tromøya is a large island off Norway’s southern coast, known for its scenic landscapes and proximity to the town of Arendal.
  • E. Flekkerøy
    Flekkerøy is a populated island and coastal community in southern Norway, known for its maritime heritage and proximity to the city of Kristiansand.
  • 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_69c0084af79c81908af128ccc29983d0 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c034a48750819099ae917ae2b54e6d completed March 22, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c113464f7481909538a1f2ef05216c completed March 23, 2026, 10:17 a.m.
Created at: March 22, 2026, 3:54 p.m.