Triple

T1139620
Position Surface form Disambiguated ID Type / Status
Subject Viking Ship Museum E23419 entity
Predicate locatedIn P40 FINISHED
Object Bygdøy E126345 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: Bygdøy | Statement: [Viking Ship Museum, locatedIn, Bygdøy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bygdøy
Context triple: [Viking Ship Museum, locatedIn, Bygdøy]
  • A. Bygdøy peninsula chosen
    The Bygdøy peninsula is a scenic and affluent area in Oslo known for its beaches, royal estate, and several of Norway’s most important museums, including the Viking Ship Museum and the Fram Museum.
  • B. Averøya
    Averøya is a scenic Norwegian island known for its coastal landscapes and its location along the famous Atlantic Ocean Road in Western Norway.
  • C. Karmøy
    Karmøy is a large island and municipality in Rogaland county, Norway, known for its coastal fishing communities, maritime heritage, and historic Viking sites.
  • D. Kragerø
    Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
  • E. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • 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_69a493ef399c8190b04b9146d2314f59 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc27c88881909c64ec30b7f66575 completed March 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69aca2e36fc081908de3b67293c7bbf6 completed March 7, 2026, 10:12 p.m.
Created at: March 1, 2026, 7:44 p.m.