Triple

T15246268
Position Surface form Disambiguated ID Type / Status
Subject Nana Coupeau E364389 entity
Predicate hasLover P9994 FINISHED
Object Fontan E347890 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: Fontan | Statement: [Nana Coupeau, hasLover, Fontan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fontan
Context triple: [Nana Coupeau, hasLover, Fontan]
  • A. Fontan chosen
    Fontan is a fictional character named Nana Fontan, likely appearing in a narrative work such as a novel, film, or television series.
  • B. Fassel
    Fassel is a surname most notably associated with Jim Fassel, a former head coach of the New York Giants in the National Football League.
  • C. Forlane
    Forlane is a graceful, dance-inspired movement from Maurice Ravel’s suite *Le Tombeau de Couperin*, known for its lilting rhythm and neoclassical elegance.
  • D. Takic
    Takic is a branch of the Uto-Aztecan language family comprising several closely related Indigenous languages historically spoken in Southern California.
  • E. Geiselwind
    Geiselwind is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural setting and proximity to major transport routes like the A3 motorway.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f4f9d48190b96a7e0c6993cd69 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd491cd881908bad9660af9b6b8f completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:13 a.m.