Triple

T17872512
Position Surface form Disambiguated ID Type / Status
Subject Coq E446868 entity
Predicate developedBy P73 FINISHED
Object INRIA 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: INRIA | Statement: [Coq, developedBy, INRIA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: INRIA
Context triple: [Coq, developedBy, INRIA]
  • A. INRIA chosen
    INRIA is the French national research institute dedicated to computer science and applied mathematics, known for its leading contributions to digital science and technology.
  • B. Laboratoire de Recherche en Informatique
    Laboratoire de Recherche en Informatique is a French computer science research laboratory known for its work in theoretical computer science, formal methods, and related areas.
  • C. Laboratoire Ampère
    Laboratoire Ampère is a French research laboratory associated with INSA Lyon that focuses on electrical engineering, automation, and related applied sciences.
  • D. Lyon research center
    The Lyon research center is a major French Petroleum Institute facility specializing in research and development in petroleum, energy, and related technologies.
  • E. Grenoble INP
    Grenoble INP is a French public engineering and technology institute in Grenoble, renowned for its network of specialized engineering schools and strong emphasis on research and innovation.
  • 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_69d8b9f4c22c819093c2680434472894 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49aa3cd248190a13a8209ba44fd3b completed April 19, 2026, 9:04 a.m.
Created at: April 10, 2026, 10:18 a.m.