Triple

T14056751
Position Surface form Disambiguated ID Type / Status
Subject M2 (Copenhagen Metro) E338238 entity
Predicate hasTerminus P388 FINISHED
Object Vanløse station E1082999 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: Vanløse station | Statement: [M2 (Copenhagen Metro), hasTerminus, Vanløse station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vanløse station
Context triple: [M2 (Copenhagen Metro), hasTerminus, Vanløse station]
  • A. Vanløse station chosen
    Vanløse station is a major public transport hub in Copenhagen that serves as an interchange between the S-train network and the Copenhagen Metro.
  • B. Fasanvej Station
    Fasanvej Station is a Copenhagen Metro station serving the Frederiksberg district of Copenhagen, Denmark.
  • C. Lindevang Station
    Lindevang Station is a Copenhagen Metro station serving the Frederiksberg district of Copenhagen, Denmark.
  • D. Værnes Station
    Værnes Station is a railway station in Stjørdal, Norway, serving passengers traveling to and from Trondheim Airport, Værnes.
  • E. Flintholm station
    Flintholm station is a major Copenhagen transport hub that serves both the Metro and S-train networks, facilitating easy transfers between multiple lines.
  • 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_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de3c8e6d008190af8892f34c5cefbd completed April 14, 2026, 1:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd54fa19c081908e6467ee7b79f02a completed May 8, 2026, 3:14 a.m.
Created at: April 9, 2026, 10:20 p.m.