Triple

T1878854
Position Surface form Disambiguated ID Type / Status
Subject The Talented Mr. Ripley E39805 entity
Predicate musicComposer P32102 FINISHED
Object Gabriel Yared E48047 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: Gabriel Yared | Statement: [The Talented Mr. Ripley, musicComposer, Gabriel Yared]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gabriel Yared
Context triple: [The Talented Mr. Ripley, musicComposer, Gabriel Yared]
  • A. Gabriel Yared chosen
    Gabriel Yared is a Lebanese-French composer renowned for his evocative film scores, including his Academy Award–winning work on "The English Patient."
  • B. Amir Mokri
    Amir Mokri is an Iranian-American cinematographer known for his dynamic, high-energy visual style on major action and blockbuster films such as "Man of Steel," "Transformers: Dark of the Moon," and "Fast & Furious."
  • C. Fady Elsayed
    Fady Elsayed is a British actor known for his roles in film and television, including the Doctor Who spin-off series "Class."
  • D. Antoine Nahas
    Antoine Nahas was a Lebanese architect best known for designing the National Museum of Beirut, a landmark institution of Lebanon’s cultural heritage.
  • E. Eli Samaha
    Eli Samaha is an American film producer known for financing and producing a range of Hollywood genre films, often through independent and mid-budget studio projects.
  • 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_69a88633e4fc8190b7eb40463e048ec5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0f8e9f08190a210440823fad69e completed March 7, 2026, 5 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf59a5b48190bb4b47641681ebb7 completed March 8, 2026, 8:43 p.m.
Created at: March 4, 2026, 7:34 p.m.