Triple

T369369
Position Surface form Disambiguated ID Type / Status
Subject Jøssingfjord E8234 entity
Predicate locatedIn P40 FINISHED
Object Rogaland county E17275 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: [Jøssingfjord, locatedIn, Rogaland county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rogaland county
Context triple: [Jøssingfjord, locatedIn, Rogaland county]
  • A. Trøndelag
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • B. Oslo County
    Oslo County is the administrative region that encompasses Norway’s capital city, Oslo, serving as a central hub for the country’s political, cultural, and academic institutions.
  • C. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • D. Western Norway chosen
    Western Norway is a coastal region of Norway known for its dramatic fjords, mountainous landscapes, and important port cities such as Bergen and Stavanger.
  • E. Southern Norway
    Southern Norway is a geographical and cultural region encompassing the southernmost part of Norway, known for its mild coastal climate, fjords, and popular holiday towns.
  • 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_69a2e7f2ec648190b42bc7db424f8109 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebfdb0608190b1794a871d0d237a completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3ecac7e048190a76c02c738599c61 completed March 1, 2026, 7:37 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.