Triple

T21831162
Position Surface form Disambiguated ID Type / Status
Subject British Rail Class 90 E538996 entity
Predicate usedBy P260 FINISHED
Object DB Cargo UK 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: DB Cargo UK | Statement: [British Rail Class 90, usedBy, DB Cargo UK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DB Cargo UK
Context triple: [British Rail Class 90, usedBy, DB Cargo UK]
  • A. DB Cargo chosen
    DB Cargo is the rail freight division of Germany’s national railway company, providing cargo transport and logistics services across Europe.
  • B. LOT Cargo
    LOT Cargo is the air freight and cargo handling division of LOT Polish Airlines, providing logistics and cargo transport services on the carrier’s route network.
  • C. GB Railfreight
    GB Railfreight is a major UK rail freight operating company that provides cargo transport services across the national rail network.
  • D. CargoNet
    CargoNet is a major Norwegian rail freight company that transports goods across Norway and into neighboring countries using the national railway network.
  • E. Kentish Express
    Kentish Express is a local newspaper serving Ashford and the surrounding area in Kent, England.
  • 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_69e0c475cda88190987d08f23caebdc1 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f091362d9081909f00ad7a2806d5cb completed April 28, 2026, 10:51 a.m.
Created at: April 16, 2026, 6:55 p.m.