Triple

T21953869
Position Surface form Disambiguated ID Type / Status
Subject Asker Station E542132 entity
Predicate servedBy P82 FINISHED
Object Flytoget 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 | Statement: [Asker Station, servedBy, Flytoget]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flytoget
Context triple: [Asker Station, servedBy, Flytoget]
  • A. Flytoget chosen
    Flytoget is Norway’s high-speed airport express train service that connects Oslo Airport with Oslo and surrounding areas.
  • B. FINN eiendom
    FINN eiendom is the real estate marketplace section of the Norwegian classifieds website Finn.no, where users can buy, sell, and rent properties.
  • C. Boende
    Boende is a town in the Democratic Republic of the Congo that serves as an important local center of trade and administration in the Tshuapa Province.
  • D. Hyra
    Hyra is the surname of American actress and producer Meg Ryan, known for her roles in popular romantic comedies of the late 20th century.
  • E. Rom Eiendom
    Rom Eiendom is a Norwegian real estate company that manages and develops properties associated with the country’s railway 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_69e0c47ef0e48190a50e1bcc43f4b3fd completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1243dfb4081909bc7a722843ffea7 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:59 p.m.