Triple
T22144773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guillaume Depardieu |
E547256
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Elisabeth Besson |
—
|
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: Elisabeth Besson | Statement: [Guillaume Depardieu, spouse, Elisabeth Besson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elisabeth Besson Context triple: [Guillaume Depardieu, spouse, Elisabeth Besson]
-
A.
Karin Viard
Karin Viard is an acclaimed French actress known for her versatile performances in both dramatic and comedic roles in contemporary French cinema.
-
B.
Nathalie Cresson
Nathalie Cresson is the daughter of Édith Cresson, the former Prime Minister of France.
-
C.
Virginie Besson-Silla
chosen
Virginie Besson-Silla is a French film producer known for her work on major science fiction and action films, including collaborations with director Luc Besson.
-
D.
Marianne Bertrand
Marianne Bertrand is a prominent economist known for her influential research on labor economics, corporate governance, and behavioral economics, particularly in the areas of discrimination and inequality.
-
E.
Francine Bergé
Francine Bergé is a French actress known for her work in mid-20th-century cinema and theater, often appearing in psychologically intense and avant-garde films.
- 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_69e11e3a95d88190a3bd80d9471976c3 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129efd52c8190ab0acff5bbfc0d77 |
completed | April 28, 2026, 9:43 p.m. |
Created at: April 16, 2026, 8:33 p.m.