Triple
T22274157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cours Simon |
E550556
|
entity |
| Predicate | hasNotableAlumni |
P51
|
FINISHED |
| Object | Sandrine Kiberlain |
—
|
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: Sandrine Kiberlain | Statement: [Cours Simon, hasNotableAlumni, Sandrine Kiberlain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sandrine Kiberlain Context triple: [Cours Simon, hasNotableAlumni, 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.
Joëlle Hache
Joëlle Hache is an editor known for her work on the film "Music Box."
-
D.
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.
-
E.
Sylvestra Le Touzel
Sylvestra Le Touzel is a British actress known for her work in film, television, and theatre, including roles in productions such as the comedy-drama "Happy-Go-Lucky."
- 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_69e11e43d8208190aff4f9cf7f2c2a8a |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f14ea547e4819098baf88f3c605242 |
completed | April 29, 2026, 12:19 a.m. |
Created at: April 16, 2026, 8:40 p.m.