Triple
T10524068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Blue |
E248249
|
entity |
| Predicate | leadActress |
P6108
|
FINISHED |
| Object | Béatrice Dalle |
E881349
|
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: Béatrice Dalle | Statement: [Betty Blue, leadActress, Béatrice Dalle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Béatrice Dalle Context triple: [Betty Blue, leadActress, Béatrice Dalle]
-
A.
Béatrice Dalle
chosen
Béatrice Dalle is a French actress known for her intense, unconventional screen presence and breakout role in the 1986 film "Betty Blue."
-
B.
Marylène Ferrand
Marylène Ferrand is a French landscape architect known for her role in designing Paris’s Parc de Bercy.
-
C.
Fanny Ardant
Fanny Ardant is a renowned French actress known for her sophisticated screen presence and acclaimed performances in European cinema and theater.
-
D.
Isabelle Adjani
Isabelle Adjani is a celebrated French actress renowned for her intense, emotionally charged performances and multiple César Awards, making her one of France’s most acclaimed film stars.
-
E.
Nathalie Baye
Nathalie Baye is an acclaimed French actress known for her versatile performances in both art-house and mainstream cinema since the 1970s.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509e155b08190996325bf484ec55d |
completed | April 7, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbb6db3f2c81908a7cb28ca8e8ebc9 |
completed | April 12, 2026, 3:14 p.m. |
Created at: April 6, 2026, 12:29 p.m.