Triple

T8791795
Position Surface form Disambiguated ID Type / Status
Subject Jean E209182 entity
Predicate exampleCompoundName P85415 FINISHED
Object Jean-Pierre E27779 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: Jean-Pierre | Statement: [Jean, exampleCompoundName, Jean-Pierre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jean-Pierre
Context triple: [Jean, exampleCompoundName, Jean-Pierre]
  • A. Jean-Pierre chosen
    Jean-Pierre is a French given name commonly used as a masculine compound first name.
  • B. Jean-Pierre Duprey
    Jean-Pierre Duprey was a French surrealist poet, sculptor, and painter associated with the postwar Surrealist movement in Paris.
  • C. Hervé
    Hervé is a French given name, often considered a variant of the English name Harvey, and is commonly used for males in French-speaking regions.
  • D. Jean-Pierre Faye
    Jean-Pierre Faye is a French writer, philosopher, and poet known for his work on narrative theory, political language, and his involvement in avant-garde literary movements.
  • E. Xavier Fabre
    Xavier Fabre is a French architect known for designing prominent cultural venues, including the Mariinsky Concert Hall in Saint Petersburg.
  • 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_69ca836240888190a62b262e56a69d2f completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f8e6e4881909155c40c52bc082c completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0f3391ff08190a725b0549fb0bc89 completed April 4, 2026, 11:17 a.m.
Created at: March 30, 2026, 6:43 p.m.