Triple
T20452718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shut In |
E501695
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Nathaniel Méchaly |
—
|
NE NERFINISHED |
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: Nathaniel Méchaly | Statement: [Shut In, musicBy, Nathaniel Méchaly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nathaniel Méchaly Context triple: [Shut In, musicBy, Nathaniel Méchaly]
-
A.
Nathaniel Méchaly
chosen
Nathaniel Méchaly is a French film composer best known for his atmospheric scores for movies such as the "Taken" series and various European thrillers.
-
B.
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.
-
C.
Gilbert Melki
Gilbert Melki is a French actor known for his versatile performances in film and television, including prominent roles in French dramas and comedies.
-
D.
Gilbert Chagoury
Gilbert Chagoury is a Nigerian-Lebanese billionaire businessman and philanthropist known for his influential role in West African commerce and politics.
-
E.
Jean-Claude Kalache
Jean-Claude Kalache is a cinematographer and lighting artist best known for his work on Pixar animated films such as Monsters, Inc.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68d039af08190827bf765b50515a8 |
completed | April 20, 2026, 8:30 p.m. |
Created at: April 16, 2026, 11:32 a.m.