Triple

T13700787
Position Surface form Disambiguated ID Type / Status
Subject L’uomo che ama E328510 entity
Predicate castMember P1668 FINISHED
Object Pierfrancesco Favino E99816 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: Pierfrancesco Favino | Statement: [L’uomo che ama, castMember, Pierfrancesco Favino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pierfrancesco Favino
Context triple: [L’uomo che ama, castMember, Pierfrancesco Favino]
  • A. Pierfrancesco Favino chosen
    Pierfrancesco Favino is an acclaimed Italian actor known for his versatile performances in both Italian cinema and international films.
  • B. Giancarlo Giannini
    Giancarlo Giannini is an acclaimed Italian actor and voice actor known for his intense performances in European cinema and international films, as well as for dubbing prominent Hollywood actors into Italian.
  • C. Gian Luca Gregori
    Gian Luca Gregori is an Italian academic who serves as the rector of Marche Polytechnic University.
  • D. Gianmarco Tognazzi
    Gianmarco Tognazzi is an Italian actor, son of famed comedian Ugo Tognazzi, known for his work in both film and television.
  • E. Giani Esposito
    Giani Esposito was a French actor and singer-songwriter known for his poetic chansons and roles in mid-20th-century French cinema.
  • 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_69d8076ff62081908a7bd79889edd7a0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc879adc88190b03f1cf815b71061 completed April 12, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdeed4548819082038c5b88ccd212 completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 9:54 p.m.