Triple

T8731365
Position Surface form Disambiguated ID Type / Status
Subject MF 77 E207261 entity
Predicate manufacturer P490 FINISHED
Object Alsthom E264558 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: Alsthom | Statement: [MF 77, manufacturer, Alsthom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alsthom
Context triple: [MF 77, manufacturer, Alsthom]
  • A. Alcatel Alsthom chosen
    Alcatel Alsthom was a major French industrial conglomerate active in telecommunications, power generation, and transport infrastructure before being restructured and rebranded as Alcatel.
  • B. Alstom (formerly Bombardier Transportation)
    Alstom (formerly Bombardier Transportation) is a major global rail transport manufacturer known for producing trains, trams, and related railway systems and equipment.
  • C. Systra
    Systra is a global engineering and consulting firm specializing in mass transit and rail infrastructure projects.
  • D. Tractebel
    Tractebel is an international engineering and consulting company specializing in energy, water, and infrastructure projects.
  • E. Siemens Transportation Systems
    Siemens Transportation Systems is a division of Siemens AG that designs and manufactures rail vehicles and related transportation infrastructure and technologies.
  • 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_69ca8358e4008190898471a59b96c301 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d27efb88190b42d5bc9774d9c63 completed March 31, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf88dc7ba88190865957c8d344fa00 completed April 3, 2026, 9:31 a.m.
Created at: March 30, 2026, 6:37 p.m.