Triple

T5449714
Position Surface form Disambiguated ID Type / Status
Subject University of Bergen E122338 entity
Predicate locatedIn P40 FINISHED
Object Vestland county E365150 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: Vestland county | Statement: [University of Bergen, locatedIn, Vestland county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vestland county
Context triple: [University of Bergen, locatedIn, Vestland county]
  • A. Nordland county
    Nordland county is a long, coastal region in northern Norway known for its dramatic fjords, islands, and Arctic landscapes.
  • B. 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.
  • C. 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.
  • D. Sogn og Fjordane chosen
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • E. Rogaland
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • 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_69bd4640f52c81909e653ec361f66d76 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd91dd747c81909892d6a9742d5fc6 completed March 20, 2026, 6:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0e2efb6d4819091ed08faec3b6c4a completed March 23, 2026, 6:51 a.m.
Created at: March 20, 2026, 2:07 p.m.