Triple
T23464904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Days, One Night |
E569077
|
entity |
| Predicate | distributor |
P1951
|
FINISHED |
| Object | Diaphana Films |
—
|
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: Diaphana Films | Statement: [Two Days, One Night, distributor, Diaphana Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diaphana Films Context triple: [Two Days, One Night, distributor, Diaphana Films]
-
A.
Diaphana Films
chosen
Diaphana Films is a French film distribution and production company known for handling acclaimed international and auteur cinema.
-
B.
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
C.
Valoria Films
Valoria Films is a film distribution company known for handling the release of various international and independent movies.
-
D.
Cineyug Films
Cineyug Films is an Indian film production company known for backing major Bollywood projects and entertainment ventures.
-
E.
Aquarius Films
Aquarius Films is an Australian film and television production company known for creating distinctive, character-driven screen content for both local and international audiences.
- 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_69e2458ebd808190b3298163132cfb0b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a6f9e59081909e8cf224ee46109c |
completed | April 29, 2026, 6:36 a.m. |
Created at: April 17, 2026, 5:54 p.m.