Triple

T1049524
Position Surface form Disambiguated ID Type / Status
Subject Arriva UK Trains E22661 entity
Predicate hasFormerSubsidiary P22179 FINISHED
Object Arriva Trains Northern E137402 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: Arriva Trains Northern | Statement: [Arriva UK Trains, hasFormerSubsidiary, Arriva Trains Northern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arriva Trains Northern
Context triple: [Arriva UK Trains, hasFormerSubsidiary, Arriva Trains Northern]
  • A. Arriva Trains Northern chosen
    Arriva Trains Northern was a former British train operating company that provided regional and commuter rail services across Northern England.
  • B. Arriva UK Trains
    Arriva UK Trains is a major British train operating company that manages several passenger rail franchises and services across the United Kingdom.
  • C. Northern Trains
    Northern Trains is a British train operating company that runs local and regional passenger rail services across Northern England.
  • D. Northern Rail
    Northern Rail is a British train operating company providing regional and commuter rail services across Northern England.
  • E. Virgin Trains
    Virgin Trains was a British train operating company under Richard Branson’s Virgin Group brand that ran long-distance passenger rail services in the UK.
  • 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_69a493da02e081908c13ff5e02a0fe7a completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bb7320f88190a8428946541df157 completed March 1, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac8f675824819091de0a83b2a6a0b3 completed March 7, 2026, 8:49 p.m.
Created at: March 1, 2026, 7:42 p.m.