Triple
T22838487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Words and Pictures |
E566010
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Juliette Binoche |
—
|
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: Juliette Binoche | Statement: [Words and Pictures, starring, Juliette Binoche]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juliette Binoche Context triple: [Words and Pictures, starring, Juliette Binoche]
-
A.
Juliette Binoche
chosen
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."
-
B.
Jean-Marie Binoche
Jean-Marie Binoche is a French actor and director best known as the father of acclaimed actress Juliette Binoche.
-
C.
Madeleine Renaud
Madeleine Renaud was a renowned French stage and film actress, celebrated for her work with the Comédie-Française and her influential partnership with director-actor Jean-Louis Barrault.
-
D.
Garance Marillier
Garance Marillier is a French actress best known for her breakout lead role in Julia Ducournau’s acclaimed horror film "Raw."
-
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 (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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e8244dc819089c0a7525fb512ab |
completed | April 29, 2026, 3:44 a.m. |
Created at: April 17, 2026, 3:35 p.m.