Triple

T19878721
Position Surface form Disambiguated ID Type / Status
Subject Paris-Saclay cluster E477707 entity
Predicate includesCompany P82128 FINISHED
Object Safran NE NERFINISHED

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: Safran | Statement: [Paris-Saclay cluster, includesCompany, Safran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Safran
Context triple: [Paris-Saclay cluster, includesCompany, Safran]
  • A. Safran chosen
    Safran is a major French multinational company specializing in aerospace, defense, and security technologies.
  • B. Safran Aircraft Engines
    Safran Aircraft Engines is a major French aerospace company that designs, develops, and manufactures aircraft and rocket engines for civil and military applications worldwide.
  • C. Baykar
    Baykar is a Turkish defense and aerospace company best known for developing and producing the Bayraktar family of unmanned combat aerial vehicles (UCAVs).
  • D. Safran Arrius 2B2Plus
    The Safran Arrius 2B2Plus is a modern turboshaft engine developed by Safran Helicopter Engines, commonly used to power light twin-engine helicopters.
  • E. Safran Landing Systems
    Safran Landing Systems is a leading global manufacturer and maintainer of aircraft landing gear and braking systems, serving commercial, regional, business, and military aviation markets.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51f32b08190b3687f4f60353250 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658dd869c81908aed91ee767f5f3d completed April 20, 2026, 4:48 p.m.
Created at: April 10, 2026, 1:52 p.m.