Triple

T3154875
Position Surface form Disambiguated ID Type / Status
Subject Franco Nero E65960 entity
Predicate name P16 FINISHED
Object Franco Nero E65960 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 Nero | Statement: [Franco Nero, name, Franco Nero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Franco Nero
Context triple: [Franco Nero, name, Franco Nero]
  • A. Franco Nero chosen
    Franco Nero is an Italian actor best known for his iconic role as the original Django in the 1966 spaghetti western and for a long, varied international film career.
  • B. Rossano Brazzi
    Rossano Brazzi was an Italian actor best known internationally for his romantic leading roles in mid-20th-century films such as "South Pacific" and "Summertime."
  • C. Adolfo Celi
    Adolfo Celi was an Italian actor and director best known internationally for his roles as suave, often villainous characters in films such as the James Bond movie "Thunderball."
  • D. Belmondo
    Belmondo is an Italian surname notably borne by Olympic champion cross-country skier Stefania Belmondo.
  • E. Lee Van Cleef
    Lee Van Cleef was an American actor best known for his sharp-featured, menacing presence in classic Westerns such as "The Good, the Bad and the Ugly" and numerous other films of the genre.
  • 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_69ad8584485081909ed529e890cadc4a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada5e7f4688190b477186254f8a572 completed March 8, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69b22503ba208190814ab2bfbe380c42 completed March 12, 2026, 2:29 a.m.
Created at: March 8, 2026, 3:05 p.m.