Triple

T21562458
Position Surface form Disambiguated ID Type / Status
Subject Blindern campus E532071 entity
Predicate locatedInNeighborhood P40 FINISHED
Object Blindern NE NERFINISHED

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: Blindern | Statement: [Blindern campus, locatedInNeighborhood, Blindern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blindern
Context triple: [Blindern campus, locatedInNeighborhood, Blindern]
  • A. Blindern chosen
    Blindern is the main campus area of the University of Oslo, known for housing several of its key faculties and academic buildings.
  • B. Grünerløkka district
    Grünerløkka district is a trendy, centrally located neighborhood in Oslo known for its vibrant street life, cafes, bars, and creative cultural scene.
  • C. Frogner district
    Frogner district is an affluent central borough of Oslo, Norway, known for its historic architecture, embassies, and the famous Frogner Park with the Vigeland sculpture installation.
  • D. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • E. Sinsen
    Sinsen is a neighborhood and major transport hub in Oslo, Norway, known for its busy traffic interchange and public transit connections.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c460db088190828c64206a450273 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eed2e5c3348190b67003e7027efa60 completed April 27, 2026, 3:07 a.m.
Created at: April 16, 2026, 6:29 p.m.