Triple

T10225481
Position Surface form Disambiguated ID Type / Status
Subject Stealing Beauty E243192 entity
Predicate stars P1956 FINISHED
Object Carlo Cecchi E846446 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: Carlo Cecchi | Statement: [Stealing Beauty, stars, Carlo Cecchi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carlo Cecchi
Context triple: [Stealing Beauty, stars, Carlo Cecchi]
  • A. Carlo Cecchi chosen
    Carlo Cecchi is an Italian actor and theater director known for his work in both art-house and mainstream European cinema.
  • B. Alfredo Ceschiatti
    Alfredo Ceschiatti was a Brazilian sculptor renowned for his modernist public sculptures, particularly those integrated into the architectural ensemble of Brasília.
  • C. Ermanno Cressoni
    Ermanno Cressoni was an influential Italian automobile designer best known for shaping Alfa Romeo’s distinctive angular design language in the 1970s and 1980s.
  • D. Paolo Seganti
    Paolo Seganti is an Italian actor known for his work in film and television, including prominent roles in both European productions and American soap operas.
  • E. Piero Piccioni
    Piero Piccioni was an Italian film composer and jazz musician known for his prolific work scoring Italian and international cinema from the 1950s through the 1980s.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d1f9cf6c81909a6b9e9b9d0a79fe completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71c8511008190a30008ed32a983d1 completed April 9, 2026, 3:27 a.m.
Created at: April 6, 2026, 11:17 a.m.