Triple

T14056764
Position Surface form Disambiguated ID Type / Status
Subject M2 (Copenhagen Metro) E338238 entity
Predicate hasStation P35 FINISHED
Object Nørreport station E1084770 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: Nørreport station | Statement: [M2 (Copenhagen Metro), hasStation, Nørreport station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nørreport station
Context triple: [M2 (Copenhagen Metro), hasStation, Nørreport station]
  • A. Nørreport station chosen
    Nørreport station is one of Copenhagen’s busiest central transport hubs, serving as a major interchange for metro, regional, and S-train services.
  • B. Frederiksberg Station
    Frederiksberg Station is a key Copenhagen Metro and S-train interchange located in the Frederiksberg district of Denmark’s capital.
  • C. Christianshavn station
    Christianshavn station is an underground Copenhagen Metro station serving the historic Christianshavn district on multiple metro lines.
  • D. Ørestad station
    Ørestad station is a major transport hub in Copenhagen’s Ørestad district, combining a Copenhagen Metro stop with regional and local train services.
  • E. Nationaltheatret station
    Nationaltheatret station is a major underground transport hub in central Oslo that serves both metro and railway lines, connecting key parts of the city and surrounding region.
  • 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.