Triple

T22330005
Position Surface form Disambiguated ID Type / Status
Subject Lambertseter Line E551997 entity
Predicate hasStation P35 FINISHED
Object Blindern station E673124 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: Blindern station | Statement: [Lambertseter Line, hasStation, Blindern station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blindern station
Context triple: [Lambertseter Line, hasStation, Blindern station]
  • A. Blindern station chosen
    Blindern station is a metro stop on Oslo’s T-bane network serving the Blindern area, home to the main campus of the University of Oslo.
  • B. Røa station
    Røa station is a suburban rapid transit stop on the Oslo Metro serving the Røa neighborhood in western Oslo, Norway.
  • C. Skøyen Station
    Skøyen Station is a major railway and commuter hub in Oslo, Norway, serving regional and local trains as part of the city's western transport corridor.
  • D. Nydalen station
    Nydalen station is an Oslo Metro station serving the Nydalen area in the Nordre Aker borough of Oslo, Norway.
  • E. Drammen Station
    Drammen Station is a major railway hub in Drammen, Norway, connecting regional and long-distance train services to Oslo and other parts of the country.
  • 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_69e11e482f788190b78d1588fc26d606 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1577a9c348190b8662142afa832be completed April 29, 2026, 12:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b17cbf58481909f01faefd9c561b5 completed May 18, 2026, 1:44 p.m.
Created at: April 16, 2026, 8:43 p.m.