Triple

T15021207
Position Surface form Disambiguated ID Type / Status
Subject Norwegian State Railways E378087 entity
Predicate successor P78 FINISHED
Object CargoNet E537560 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: CargoNet | Statement: [Norwegian State Railways, successor, CargoNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CargoNet
Context triple: [Norwegian State Railways, successor, CargoNet]
  • A. CargoNet chosen
    CargoNet is a major Norwegian rail freight company that transports goods across Norway and into neighboring countries using the national railway network.
  • B. DB Cargo
    DB Cargo is the rail freight division of Germany’s national railway company, providing cargo transport and logistics services across Europe.
  • C. Cargo
    Cargo is Rust’s official build and dependency management tool that streamlines compiling code, managing libraries, and distributing Rust packages.
  • D. Cargo
    Cargo is a small rural town in the Central West region of New South Wales, Australia, known for its agricultural surroundings and village community.
  • E. Cargo
    Cargo is an Australian post-apocalyptic horror drama film best known for its emotional story of a father trying to save his infant daughter during a zombie outbreak.
  • 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_69d85cd3a3c881908c71fc424d459c17 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded765462c819097f331c9b39c80e3 completed April 15, 2026, 12:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe9dd2c96c8190a0368678584aaa16 completed May 9, 2026, 2:37 a.m.
Created at: April 10, 2026, 2:56 a.m.