Triple

T14001998
Position Surface form Disambiguated ID Type / Status
Subject Boknafjorden E336848 entity
Predicate hasIsland P970 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: [Boknafjorden, hasIsland, Karmøy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karmøy
Context triple: [Boknafjorden, hasIsland, 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. Kvitsøy
    Kvitsøy is a small island municipality in southwestern Norway known for its maritime heritage, lighthouse, and rich coastal fishing grounds.
  • E. Tromøya
    Tromøya is a large island off Norway’s southern coast, known for its scenic landscapes and proximity to the town of Arendal.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed06a50819093ddc64f55050689 completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0139e10e94819092b71606dbe4f5d5 completed May 11, 2026, 2:07 a.m.
Created at: April 9, 2026, 10:19 p.m.