Triple

T2981765
Position Surface form Disambiguated ID Type / Status
Subject Cimetière des Batignolles E80527 entity
Predicate hasGraveOf P196 FINISHED
Object France Gall
France Gall was a popular French yé-yé singer and Eurovision winner who became a major figure in French pop music from the 1960s onward.
 E316216 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: France Gall | Statement: [Cimetière des Batignolles, hasGraveOf, France Gall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: France Gall
Context triple: [Cimetière des Batignolles, hasGraveOf, France Gall]
  • A. Fran
    Fran is a common shortened given name, typically used as a diminutive of Frances or Francis.
  • B. Germain of Paris
    Germain of Paris was a 6th-century Bishop of Paris venerated as a saint for his piety, charity, and influence on the early Frankish church.
  • C. Elle France
    Elle France is the French edition of the international women's fashion and lifestyle magazine Elle, known for its coverage of style, beauty, culture, and current affairs.
  • D. France Bélisle
    France Bélisle is a Canadian politician who serves as the mayor of Gatineau, Quebec.
  • E. Valois
    Valois was a prominent French royal dynasty that ruled France during the late Middle Ages and Renaissance, succeeding the Capetians and preceding the Bourbons.
  • 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: France Gall
Triple: [Cimetière des Batignolles, hasGraveOf, France Gall]
Generated description
France Gall was a popular French yé-yé singer and Eurovision winner who became a major figure in French pop music from the 1960s onward.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: France Gall
Target entity description: France Gall was a popular French yé-yé singer and Eurovision winner who became a major figure in French pop music from the 1960s onward.
  • A. Fran
    Fran is a common shortened given name, typically used as a diminutive of Frances or Francis.
  • B. Germain of Paris
    Germain of Paris was a 6th-century Bishop of Paris venerated as a saint for his piety, charity, and influence on the early Frankish church.
  • C. Elle France
    Elle France is the French edition of the international women's fashion and lifestyle magazine Elle, known for its coverage of style, beauty, culture, and current affairs.
  • D. France Bélisle
    France Bélisle is a Canadian politician who serves as the mayor of Gatineau, Quebec.
  • E. Valois
    Valois was a prominent French royal dynasty that ruled France during the late Middle Ages and Renaissance, succeeding the Capetians and preceding the Bourbons.
  • 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_69ad8b15f6ac8190be5fd16a33edcb4f completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99a098e08190976eb4b019818f67 completed March 8, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108f27d648190a7a58670fec8b74d completed March 11, 2026, 6:17 a.m.
NEDg Description generation batch_69b10bda5d848190af553c5f245b165d completed March 11, 2026, 6:29 a.m.
NED2 Entity disambiguation (via description) batch_69b10c9198288190a3e3ea7112ea4460 completed March 11, 2026, 6:32 a.m.
Created at: March 8, 2026, 2:58 p.m.