Triple

T5652453
Position Surface form Disambiguated ID Type / Status
Subject Norwegian railway network E124536 entity
Predicate passengerServiceOperators P31751 FINISHED
Object SJ Norge E119734 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: SJ Norge | Statement: [Norwegian railway network, passengerServiceOperators, SJ Norge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SJ Norge
Context triple: [Norwegian railway network, passengerServiceOperators, SJ Norge]
  • A. SJ Norge chosen
    SJ Norge is a Norwegian railway company operating passenger train services on key routes across Norway.
  • B. Osedalen
    Osedalen is a village in Froland municipality in Agder county in southern Norway.
  • C. Storlien
    Storlien is a village and ski resort in central Sweden near the Norwegian border, known for its winter sports and cross-border rail connections.
  • D. Sæbø
    Sæbø is a small Norwegian village known for its scenic location amid steep mountains and fjord landscapes in western Norway.
  • E. Skedsmo
    Skedsmo is a former municipality in Viken county, Norway, located northeast of Oslo and known for its suburban communities and historical ties to the Oslo 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_69c00825df388190a58742fa9b1aa33d completed March 22, 2026, 3:17 p.m.
NER Named-entity recognition batch_69c022d8a2588190b10de59edbc8841f completed March 22, 2026, 5:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d941ea4819084bab644d6cc2153 completed March 22, 2026, 8:14 p.m.
Created at: March 22, 2026, 3:42 p.m.