Triple

T8457538
Position Surface form Disambiguated ID Type / Status
Subject MP 14 E199957 entity
Predicate manufacturer P490 FINISHED
Object Alstom E25910 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: Alstom | Statement: [MP 14, manufacturer, Alstom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alstom
Context triple: [MP 14, manufacturer, Alstom]
  • A. Alstom (formerly Bombardier Transportation) chosen
    Alstom (formerly Bombardier Transportation) is a major global rail transport manufacturer known for producing trains, trams, and related railway systems and equipment.
  • B. Alcatel Alsthom
    Alcatel Alsthom was a major French industrial conglomerate active in telecommunications, power generation, and transport infrastructure before being restructured and rebranded as Alcatel.
  • C. Siemens Transportation Systems
    Siemens Transportation Systems is a division of Siemens AG that designs and manufactures rail vehicles and related transportation infrastructure and technologies.
  • D. Alstom Juniper
    Alstom Juniper is a family of electric multiple unit trains built by Alstom for use on the British railway network.
  • E. Hitachi Rail
    Hitachi Rail is a global rail transport and engineering company that designs, manufactures, and maintains trains and railway systems for urban, intercity, and high-speed networks.
  • 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_69ca8318231881908fd1bc1c4d45d286 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe49064f881909391d565b97e9886 completed March 31, 2026, 3:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea82c897c8190b7554d2e91968b53 completed April 2, 2026, 5:32 p.m.
Created at: March 30, 2026, 6:10 p.m.