Triple

T5513067
Position Surface form Disambiguated ID Type / Status
Subject Jean Zay E144612 entity
Predicate owner P347 FINISHED
Object CNRS E54266 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: CNRS | Statement: [Jean Zay, owner, CNRS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CNRS
Context triple: [Jean Zay, owner, CNRS]
  • A. Centre National de la Recherche Scientifique chosen
    The Centre National de la Recherche Scientifique (CNRS) is France’s largest governmental research organization, renowned for its multidisciplinary scientific research across fields ranging from physics and biology to the humanities.
  • B. Institut national de la recherche scientifique
    Institut national de la recherche scientifique is a graduate-level research university in Quebec, Canada, specializing in advanced scientific and technological research and training.
  • C. INSERM
    INSERM is France’s national public research institute dedicated to human health and medical research.
  • D. Institut des Hautes Études Scientifiques
    The Institut des Hautes Études Scientifiques is a prestigious French research institute renowned for its fundamental work in mathematics and theoretical physics.
  • E. INRIA
    INRIA is the French national research institute dedicated to computer science and applied mathematics, known for its leading contributions to digital science and technology.
  • 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_69c008f77ff88190b0cd50ca207295d1 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01f599d0881909ce86fcc45d4d920 completed March 22, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04cc0735881909b7ea6909570a750 completed March 22, 2026, 8:10 p.m.
Created at: March 22, 2026, 3:33 p.m.