Triple
T16968183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bullet Train (2022 film) |
E411596
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Jonathan Sela |
E219932
|
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: Jonathan Sela | Statement: [Bullet Train (2022 film), cinematographyBy, Jonathan Sela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Sela Context triple: [Bullet Train (2022 film), cinematographyBy, Jonathan Sela]
-
A.
Jonathan Sela
chosen
Jonathan Sela is a cinematographer known for his dynamic, high-energy visual work on major action films and thrillers.
-
B.
Jonathan Benassaya
Jonathan Benassaya is a French entrepreneur best known for co-founding the music streaming service Deezer.
-
C.
Joshua Sternin
Joshua Sternin is an American television writer and producer known for his work on animated and live-action series, including co-creating the 2012 Teenage Mutant Ninja Turtles TV adaptation.
-
D.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
E.
Jake Kassan
Jake Kassan is an American entrepreneur best known as the co-founder of the minimalist watch and accessories brand MVMT.
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0a6f628819080db47285954729a |
completed | April 18, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d46f1d608190befe4dcbda086c03 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:31 a.m.