Triple

T12975661
Position Surface form Disambiguated ID Type / Status
Subject Vinci Airports E321516 entity
Predicate subsidiaryOf P254 FINISHED
Object Vinci Concessions E321520 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: Vinci Concessions | Statement: [Vinci Airports, subsidiaryOf, Vinci Concessions]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vinci Concessions
Context triple: [Vinci Airports, subsidiaryOf, Vinci Concessions]
  • A. Vinci SA chosen
    Vinci SA is a major French multinational concessions and construction company specializing in infrastructure development and management worldwide.
  • B. Vinci Construction
    Vinci Construction is a major global construction and civil engineering company specializing in large-scale infrastructure and building projects.
  • C. Vingroup
    Vingroup is one of Vietnam’s largest private conglomerates, known for its extensive investments in real estate, retail, technology, and automotive manufacturing through brands like Vinhomes and VinFast.
  • D. Nentico
    Nentico is an alternative name for the Nanticoke, a Native American people historically located in the Mid-Atlantic region of the United States.
  • E. FLC Group
    FLC Group is a Vietnamese multi-industry conglomerate known for its investments in real estate, hospitality, aviation, and other sectors.
  • 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_69f6c0f702fc8190936a7dd292a675f8 completed May 3, 2026, 3:28 a.m.
Created at: April 9, 2026, 8:37 p.m.