Triple

T11210274
Position Surface form Disambiguated ID Type / Status
Subject INRIA E265286 entity
Predicate abbreviation P43 FINISHED
Object INRIA E265286 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: INRIA | Statement: [INRIA, abbreviation, INRIA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: INRIA
Context triple: [INRIA, abbreviation, 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. 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.
  • C. INSA Lyon
    INSA Lyon is a leading French grande école and engineering school located near Lyon, renowned for its strong research activity and multidisciplinary engineering programs.
  • D. INSA Rennes
    INSA Rennes is a leading French public engineering school located in Rennes, specializing in science and technology education and research.
  • E. ENS Paris-Saclay
    ENS Paris-Saclay is a prestigious French grande école that trains high-level researchers, academics, and professionals in science and humanities within the Université Paris-Saclay system.
  • 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_69d6aac59460819089b9848b27f57848 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8d6f5d4819086dcb776a0d469e8 completed April 9, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4cc2ec5348190b66a3cc5779aa327 completed April 19, 2026, 12:35 p.m.
Created at: April 8, 2026, 9:30 p.m.