Triple

T7549547
Position Surface form Disambiguated ID Type / Status
Subject Lady Sings the Blues E178494 entity
Predicate cinematographyBy P1953 FINISHED
Object Michel Hugo E756155 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: Michel Hugo | Statement: [Lady Sings the Blues, cinematographyBy, Michel Hugo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michel Hugo
Context triple: [Lady Sings the Blues, cinematographyBy, Michel Hugo]
  • A. Michel Hugo chosen
    Michel Hugo was a French-born cinematographer known for his work on American film and television productions in the late 20th century.
  • B. Michel Regembal
    Michel Regembal is a French architect best known as one of the designers of the Stade de France, the national stadium located in Saint-Denis near Paris.
  • C. Georges Hugon
    Georges Hugon is a character in Émile Zola’s novel "Nana," depicted as a young aristocrat whose infatuation with the courtesan Nana leads to his moral and financial ruin.
  • D. Michel Bouvier
    Michel Bouvier is a biochemist and entrepreneur known for his pioneering work on G protein–coupled receptors (GPCRs) and for co-founding innovative drug discovery companies.
  • E. Michel Desvigne
    Michel Desvigne is a renowned French landscape architect known for large-scale urban public space projects and influential contemporary landscape designs.
  • 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_69c69f2cbe08819088f9eb0c03ef529b completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f8b35ba481908e1e5bbf329daa33 completed March 27, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6e2cd9cc8190accfefed5eaa0b07 completed April 3, 2026, 7:37 a.m.
Created at: March 27, 2026, 3:49 p.m.