Triple
T6126763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Zero Theorem |
E136613
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Mélanie Thierry |
E219101
|
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: Mélanie Thierry | Statement: [The Zero Theorem, castMember, Mélanie Thierry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mélanie Thierry Context triple: [The Zero Theorem, castMember, Mélanie Thierry]
-
A.
Melanie Thierry
chosen
Melanie Thierry is a French actress and former model known for her roles in both European cinema and international films.
-
B.
Nelly Auteuil
Nelly Auteuil is the daughter of French actor and filmmaker Daniel Auteuil.
-
C.
Virginie Ledoyen
Virginie Ledoyen is a French actress known for her work in both French cinema and international films, including prominent roles in dramas and thrillers.
-
D.
Sandrine Bonnaire
Sandrine Bonnaire is an acclaimed French actress and filmmaker known for her powerful performances in films such as "Vagabond" and "Under the Sun of Satan."
-
E.
Juliette Binoche
Juliette Binoche is an acclaimed French actress known for her nuanced performances in international cinema and her Academy Award-winning role in "The English Patient."
- 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_69c008a0a37c81908e5b4f879158afb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c2a13a48190b80e11d58fc87c8a |
completed | March 22, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c135c44f008190bdef195511fe1111 |
completed | March 23, 2026, 12:44 p.m. |
Created at: March 22, 2026, 4:15 p.m.