Triple

T12620950
Position Surface form Disambiguated ID Type / Status
Subject Rolvsøy E301376 entity
Predicate formerSubdivisionOf P30672 FINISHED
Object Østfold county E96296 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: Østfold county | Statement: [Rolvsøy, formerSubdivisionOf, Østfold county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Østfold county
Context triple: [Rolvsøy, formerSubdivisionOf, Østfold county]
  • A. Østfold chosen
    Østfold is a former county in southeastern Norway known for its coastal landscape along the Oslofjord, historic fortresses, and proximity to the Swedish border.
  • 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. Agder
    Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
  • D. Aust-Agder
    Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
  • 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_69d7bdeaf49c8190b13800111fa77ea3 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d960c75c9c819092265ebc2b39f21d completed April 10, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c6f8e9881908f350aa3cefef269 completed May 2, 2026, 10:36 p.m.
Created at: April 9, 2026, 5:13 p.m.