Triple

T12432451
Position Surface form Disambiguated ID Type / Status
Subject Pharo Smalltalk E297064 entity
Predicate developer P73 FINISHED
Object Inria RMoD team 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 RMoD team | Statement: [Pharo Smalltalk, developer, Inria RMoD team]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Inria RMoD team
Context triple: [Pharo Smalltalk, developer, Inria RMoD team]
  • 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. INSA Rennes
    INSA Rennes is a leading French public engineering school located in Rennes, specializing in science and technology education and research.
  • C. VERIMAG Laboratory
    VERIMAG Laboratory is a French research center specializing in formal methods and the verification of complex hardware and software systems.
  • D. 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.
  • E. Centre de robotique
    The Centre de robotique is a research and teaching unit specializing in robotics and related technologies within the École des Mines de Paris engineering school.
  • 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_69d6ada0640c81908c061d7fb3d47786 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d7f2fd08190ab959742dbd8f9c0 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6349d29c481909a37fd386cc06575 completed May 2, 2026, 5:30 p.m.
Created at: April 8, 2026, 9:55 p.m.