Triple

T8826838
Position Surface form Disambiguated ID Type / Status
Subject BB 26000 E210035 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: [BB 26000, manufacturer, Alsthom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alsthom
Context triple: [BB 26000, 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_69ca8365b28081909e48e45e95dfc405 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc6034e8dc819099116d772e87569a completed April 1, 2026, midnight
NED1 Entity disambiguation (via context triple) batch_69cfab71962c8190823a1ca1d4fa56f3 completed April 3, 2026, 11:58 a.m.
Created at: March 30, 2026, 6:46 p.m.