Triple
T5531571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damage (1992 film) |
E145060
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Peter Biziou |
E235553
|
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: Peter Biziou | Statement: [Damage (1992 film), cinematographyBy, Peter Biziou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Biziou Context triple: [Damage (1992 film), cinematographyBy, Peter Biziou]
-
A.
Peter Biziou
chosen
Peter Biziou is a British cinematographer known for his work on films such as "Bugsy Malone" and the Oscar-winning "Mississippi Burning."
-
B.
Benoît Delhomme
Benoît Delhomme is a French cinematographer known for his visually distinctive work on international films such as The Scent of Green Papaya, The Theory of Everything, and Lawless.
-
C.
Frédéric Bricout
Frédéric Bricout is a French politician who serves as the mayor of the northern French city of Cambrai.
-
D.
Roland Gallois
Roland Gallois is a film editor known for his work on the feature film "Slow West."
-
E.
Jean-Claude Olivier
Jean-Claude Olivier is a writer associated with the Juicy brand or publication.
- 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_69c008f9955881909bfa8348b56b4739 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f9d17ec8190b93b12931a4c1b33 |
completed | March 22, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c141085da88190bf5cd0a90914f929 |
completed | March 23, 2026, 1:32 p.m. |
Created at: March 22, 2026, 3:34 p.m.