Triple

T20707385
Position Surface form Disambiguated ID Type / Status
Subject Flytoget airport express train E508940 entity
Predicate operator P179 FINISHED
Object Flytoget AS 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: Flytoget AS | Statement: [Flytoget airport express train, operator, Flytoget AS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flytoget AS
Context triple: [Flytoget airport express train, operator, Flytoget AS]
  • A. Norwegian Property ASA
    Norwegian Property ASA is a Norwegian real estate investment company specializing in the ownership, development, and management of prime commercial properties.
  • B. Flytoget chosen
    Flytoget is Norway’s high-speed airport express train service that connects Oslo Airport with Oslo and surrounding areas.
  • C. Skanska Norge
    Skanska Norge is the Norwegian subsidiary of the multinational construction and development company Skanska, responsible for major building and infrastructure projects across Norway.
  • D. Rom Eiendom
    Rom Eiendom is a Norwegian real estate company that manages and develops properties associated with the country’s railway infrastructure.
  • E. Remgro
    Remgro is a South African investment holding company with a diversified portfolio spanning sectors such as financial services, healthcare, consumer products, and infrastructure.
  • 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_69e0b4c40ad88190b81f77695366d328 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c1917ce08190a54720d4d5b0a02c completed April 21, 2026, 12:15 a.m.
Created at: April 16, 2026, 12:14 p.m.