Triple

T2005911
Position Surface form Disambiguated ID Type / Status
Subject Lysaker E43583 entity
Predicate adjacentTo P224 FINISHED
Object Fornebu 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: Fornebu | Statement: [Lysaker, adjacentTo, Fornebu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fornebu
Context triple: [Lysaker, adjacentTo, Fornebu]
  • A. Sinsen
    Sinsen is a neighborhood and major transport hub in Oslo, Norway, known for its busy traffic interchange and public transit connections.
  • B. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • C. Bærum chosen
    Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
  • D. 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.
  • E. Porsgrunn
    Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
  • 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_69a88715dbbc8190b2299e29e955d997 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb898795481909920c1a4c4d62c2d completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71a7e3408190955aa7f2534316dc completed March 9, 2026, 7:07 a.m.
Created at: March 4, 2026, 7:37 p.m.