Triple

T3101989
Position Surface form Disambiguated ID Type / Status
Subject Finnøy E64739 entity
Predicate partOf P40 FINISHED
Object Rogaland county E139000 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: Rogaland county | Statement: [Finnøy, partOf, Rogaland county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rogaland county
Context triple: [Finnøy, partOf, Rogaland county]
  • A. Rogaland chosen
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • B. Oslo county
    Oslo county is Norway’s capital county, encompassing the city of Oslo and serving as the country’s political, economic, and cultural center.
  • C. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • D. Akershus county
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • E. Vestfold og Telemark
    Vestfold og Telemark is a former county in southeastern Norway known for its coastal towns, industrial heritage, and varied landscapes from fjords to inland forests and mountains.
  • 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_69ad857dc98481909e585dc3372e3ed5 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada26c76ec81908d11f82be573c518 completed March 8, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b28e058d8c8190ae58d750ae4c2c0e completed March 12, 2026, 9:57 a.m.
Created at: March 8, 2026, 3:03 p.m.