Triple
T6960505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Léa Seydoux |
E161355
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Léa Seydoux |
E161355
|
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: Léa Seydoux | Statement: [Léa Seydoux, name, Léa Seydoux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Léa Seydoux Context triple: [Léa Seydoux, name, Léa Seydoux]
-
A.
Lea Seydoux
chosen
Léa Seydoux is a French actress known for her roles in films such as "Blue Is the Warmest Colour," multiple James Bond movies, and various international arthouse and blockbuster productions.
-
B.
Ariane Labed
Ariane Labed is a French-Greek actress known for her work in art-house and independent cinema, often collaborating with acclaimed directors in critically praised films.
-
C.
Sofia Boutella
Sofia Boutella is an Algerian-French dancer and actress known for her dynamic action roles in films such as "Kingsman: The Secret Service," "Star Trek Beyond," and "The Mummy."
-
D.
Bérénice Marlohe
Bérénice Marlohe is a French actress best known internationally for her role as Sévérine in the James Bond film "Skyfall."
-
E.
Clémence Poésy
Clémence Poésy is a French actress and model known for her roles in films such as the Harry Potter series, In Bruges, and various French and international productions.
- 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6daeee4d48190b078beeebb0053f4 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7618df8648190bec6c0aaca312efd |
completed | March 28, 2026, 5:05 a.m. |
Created at: March 27, 2026, 2:29 p.m.