Triple

T14109571
Position Surface form Disambiguated ID Type / Status
Subject DSB E339598 entity
Predicate translatedName P2303 FINISHED
Object Danish State Railways E1079506 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: Danish State Railways | Statement: [DSB, translatedName, Danish State Railways]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Danish State Railways
Context triple: [DSB, translatedName, Danish State Railways]
  • A. Danske Statsbaner chosen
    Danske Statsbaner is Denmark’s largest state-owned railway company, responsible for most passenger train services across the country.
  • B. Norwegian State Railways
    Norwegian State Railways was Norway’s former government-owned railway company responsible for most passenger train services and rail operations across the country.
  • C. Finland Railway
    Finland Railway was a historical railway company that operated key lines in the Russian Empire, including the route terminating at Saint Petersburg’s Finland Station.
  • D. Metro Service A/S
    Metro Service A/S is the company responsible for operating and managing the Copenhagen Metro system in Denmark.
  • E. Aarhus Letbane
    Aarhus Letbane is a light rail transit system serving the city of Aarhus and surrounding areas in Denmark.
  • 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_69d81c69b5c8819094aa1abf18302908 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de600caf308190ab6d8451ed4e3797 completed April 14, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdf04871c8190891605415f1abf7f completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 10:22 p.m.