Triple

T12975829
Position Surface form Disambiguated ID Type / Status
Subject Vinci SA E321520 entity
Predicate formerName P65 FINISHED
Object GTM E555236 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: GTM | Statement: [Vinci SA, formerName, GTM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GTM
Context triple: [Vinci SA, formerName, GTM]
  • A. GTM
    GTM is the three-letter ISO 3166-1 alpha-3 country code assigned to Guatemala.
  • B. GTM chosen
    GTM was a former name of Vinci, the major French concessions and construction company involved in large-scale infrastructure projects worldwide.
  • C. GTM 33
    GTM 33 is the standard abbreviation for the influential graduate-level textbook "Differential Topology" in Springer’s Graduate Texts in Mathematics series.
  • D. GTM 218
    GTM 218 is the standard abbreviation for John M. Lee’s widely used graduate-level textbook "Introduction to Smooth Manifolds" in the Springer Graduate Texts in Mathematics series.
  • E. Google Tag Manager
    Google Tag Manager is a tag management system that lets marketers and developers easily add, update, and manage tracking and analytics tags on websites and apps without modifying the underlying code directly.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e48c0208190bb7ec80780480b37 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8ec821c81909398d8e02d69dcbf completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 8:37 p.m.