Triple
T2216648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabriel Yared |
E48047
|
entity |
| Predicate | name |
P16
|
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: [Gabriel Yared, name, Gabriel Yared]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gabriel Yared Context triple: [Gabriel Yared, name, 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_69a88aa1ee708190862c8c378c41e9eb |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc00f4c3881909d03301fcdfa8b67 |
completed | March 7, 2026, 6:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae65594c948190a43bdab03e61c130 |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:46 p.m.