Triple

T400949
Position Surface form Disambiguated ID Type / Status
Subject Østfold University College E9278 entity
Predicate regionServed P82 FINISHED
Object Østfold region E50816 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 region | Statement: [Østfold University College, regionServed, Østfold region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Østfold region
Context triple: [Østfold University College, regionServed, Østfold region]
  • A. Trøndelag
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • B. Funen
    Funen is Denmark’s third-largest island, located between the Jutland Peninsula and Zealand and known for its rolling countryside and the city of Odense, birthplace of Hans Christian Andersen.
  • C. Viken county chosen
    Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
  • D. Uppsala County
    Uppsala County is an administrative region in east-central Sweden known for its historic university city of Uppsala and its mix of cultural heritage and rural landscapes.
  • E. Herning
    Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
  • 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ec9f77888190bcc2bc68d201ed35 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4239e5bcc8190918c5c90c77898c9 completed March 1, 2026, 11:31 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.