Triple

T3882254
Position Surface form Disambiguated ID Type / Status
Subject Akershus E92851 entity
Predicate contains P35 FINISHED
Object Bærum E186288 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: Bærum | Statement: [Akershus, contains, Bærum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bærum
Context triple: [Akershus, contains, Bærum]
  • A. Bærum chosen
    Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
  • B. Drammen
    Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
  • C. Sandnes
    Sandnes is a city in southwestern Norway, near Stavanger, known for its proximity to fjords and outdoor recreation areas.
  • D. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • E. Gjøvik
    Gjøvik is a town and municipality in Innlandet county, Norway, known for its location along Lake Mjøsa and its mix of industrial heritage and modern sports and cultural facilities.
  • 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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec8e8b3481909617ca0e37f8a6d4 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d044ec6c81909e2abcb8c429045a completed March 14, 2026, 9:16 p.m.
Created at: March 9, 2026, 3:20 p.m.