Triple

T17309153
Position Surface form Disambiguated ID Type / Status
Subject Kvam Municipality E420244 entity
Predicate hasSettlement P1068 FINISHED
Object Øystese 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: Øystese | Statement: [Kvam Municipality, hasSettlement, Øystese]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Øystese
Context triple: [Kvam Municipality, hasSettlement, Øystese]
  • A. Øystese chosen
    Øystese is a village in Vestland county, Norway, known for its scenic fjordside setting and role as a local center within the municipality of Kvam.
  • B. Hisøy
    Hisøy is an island in southern Norway that forms part of the coastal region of Agder, known for its maritime character and proximity to the town of Arendal.
  • C. Osøyro
    Osøyro is the administrative and commercial center of Bjørnafjorden Municipality in Vestland county, Norway.
  • D. Sørkjosen
    Sørkjosen is a small coastal village in Northern Norway known as a gateway to the Reisa valley and Reisa National Park.
  • E. 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.
  • 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_69d889d22b848190a4663d0b8f8f76e7 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e439970cf08190bc9e49ba830da0d9 completed April 19, 2026, 2:10 a.m.
Created at: April 10, 2026, 5:43 a.m.