Triple
T14806699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cours Florent |
E348055
|
entity |
| Predicate | notableAlumni |
P51
|
FINISHED |
| Object | Diane Kruger |
E31747
|
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: Diane Kruger | Statement: [Cours Florent, notableAlumni, Diane Kruger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diane Kruger Context triple: [Cours Florent, notableAlumni, Diane Kruger]
-
A.
Diane Kruger
chosen
Diane Kruger is a German-born actress and former fashion model best known for her roles in films such as "Troy," "Inglourious Basterds," and "National Treasure."
-
B.
Franka Potente
Franka Potente is a German actress best known internationally for her breakout role in "Run Lola Run" and her appearances in the Bourne film series.
-
C.
Laetitia Casta
Laetitia Casta is a French supermodel and actress known for her work with major fashion houses and her roles in European cinema.
-
D.
Juliette Binoche
Juliette Binoche is an acclaimed French actress known for her nuanced performances in international cinema and her Academy Award-winning role in "The English Patient."
-
E.
Emmanuelle Béart
Emmanuelle Béart is a French actress acclaimed for her performances in films such as "Manon des Sources" and "Mission: Impossible."
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decf33b6a08190ab6a4cfeda2cc09c |
completed | April 14, 2026, 11:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe64f6c6148190941b05d06d4dc54d |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:40 a.m.