Triple

T11713463
Position Surface form Disambiguated ID Type / Status
Subject Princesse Tam-Tam E278429 entity
Predicate castMember P1668 FINISHED
Object Jean Galland
Jean Galland was a French film and stage actor active in the early to mid-20th century.
E942071 NE FINISHED

How this triple was built (4 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 Galland | Statement: [Princesse Tam-Tam, castMember, Jean Galland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jean Galland
Context triple: [Princesse Tam-Tam, castMember, Jean Galland]
  • A. François Delarozière
    François Delarozière is a French artist and designer renowned for his monumental mechanical sculptures and fantastical urban installations.
  • B. Bertrand Morane
    Bertrand Morane is the obsessive, womanizing protagonist of François Truffaut’s film "The Man Who Loved Women," whose life is defined by his compulsive pursuit of romantic and sexual relationships.
  • C. Serge Dassault
    Serge Dassault was a French industrialist, politician, and billionaire who led the Dassault aviation and media empire.
  • D. Marcel Dassault
    Marcel Dassault was a prominent French aircraft industrialist, engineer, and politician who founded the Dassault aviation empire.
  • E. Serge Kampf
    Serge Kampf was a French entrepreneur best known as the founder and longtime leader of the global consulting and IT services company Capgemini.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jean Galland
Triple: [Princesse Tam-Tam, castMember, Jean Galland]
Generated description
Jean Galland was a French film and stage actor active in the early to mid-20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Jean Galland
Target entity description: Jean Galland was a French film and stage actor active in the early to mid-20th century.
  • A. François Delarozière
    François Delarozière is a French artist and designer renowned for his monumental mechanical sculptures and fantastical urban installations.
  • B. Bertrand Morane
    Bertrand Morane is the obsessive, womanizing protagonist of François Truffaut’s film "The Man Who Loved Women," whose life is defined by his compulsive pursuit of romantic and sexual relationships.
  • C. Serge Dassault
    Serge Dassault was a French industrialist, politician, and billionaire who led the Dassault aviation and media empire.
  • D. Marcel Dassault
    Marcel Dassault was a prominent French aircraft industrialist, engineer, and politician who founded the Dassault aviation empire.
  • E. Serge Kampf
    Serge Kampf was a French entrepreneur best known as the founder and longtime leader of the global consulting and IT services company Capgemini.
  • F. None of above. chosen

Provenance (5 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4be10088190854699385d1f6a95 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef838562d08190b9a764e88c50d423 completed April 27, 2026, 3:40 p.m.
NEDg Description generation batch_69ef9b68309081909f3f614efeeb2ab1 completed April 27, 2026, 5:22 p.m.
NED2 Entity disambiguation (via description) batch_69efd6aba82c81909ff22e6b26db3cfe completed April 27, 2026, 9:35 p.m.
Created at: April 8, 2026, 9:40 p.m.