Triple

T13393881
Position Surface form Disambiguated ID Type / Status
Subject Franco Interlenghi E319647 entity
Predicate givenName P17 FINISHED
Object Franco E343435 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: Franco | Statement: [Franco Interlenghi, givenName, Franco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Franco
Context triple: [Franco Interlenghi, givenName, Franco]
  • A. Franco chosen
    Franco is the given name of Franco Harris, the Hall of Fame NFL running back best known for the “Immaculate Reception” with the Pittsburgh Steelers.
  • B. Benito
    Benito is the given name of Benito Mussolini, the Italian dictator who founded and led the National Fascist Party and ruled Italy from the 1920s until 1943.
  • C. Ferdinande
    Ferdinande is the given name of Archduchess Auguste Ferdinande of Austria, a 19th-century member of the Habsburg-Lorraine dynasty.
  • D. Francesco Francia
    Francesco Francia was an Italian Renaissance painter and goldsmith from Bologna, known for his refined religious altarpieces and influential role in the Bolognese school of painting.
  • E. Francois
    Francois is the given first name of South African rugby union scrum-half Faf de Klerk.
  • 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_69d806b886bc8190b676e7768b8e01c5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dba0d74e5881909828854bba7d9a87 completed April 12, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7306fb8348190a325a07a1ac858fd completed May 3, 2026, 11:24 a.m.
Created at: April 9, 2026, 9:34 p.m.