Triple
T10323057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esther Kahn |
E242687
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Sandrine Kiberlain |
E574929
|
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: Sandrine Kiberlain | Statement: [Esther Kahn, hasCastMember, Sandrine Kiberlain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sandrine Kiberlain Context triple: [Esther Kahn, hasCastMember, Sandrine Kiberlain]
-
A.
Sandrine Kiberlain
chosen
Sandrine Kiberlain is a French actress and singer known for her acclaimed performances in both dramatic and comedic films.
-
B.
Nelly Auteuil
Nelly Auteuil is the daughter of French actor and filmmaker Daniel Auteuil.
-
C.
Sandrine Holt
Sandrine Holt is a Canadian actress known for her roles in film and television, including appearances in genre franchises and high-profile dramas.
-
D.
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.
-
E.
Ludivine Sagnier
Ludivine Sagnier is a French actress known for her versatile performances in both art-house and mainstream films, as well as in international television series.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d6cdb6cc8190b37ca4494287128b |
completed | April 7, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7503f3df88190bc5acb5e5295f787 |
completed | April 9, 2026, 7:07 a.m. |
Created at: April 6, 2026, 11:50 a.m.