Triple
T16884241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Everything Everywhere All at Once |
E421496
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Larkin Seiple |
E421496
|
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: Larkin Seiple | Statement: [Everything Everywhere All at Once, cinematographyBy, Larkin Seiple]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larkin Seiple Context triple: [Everything Everywhere All at Once, cinematographyBy, Larkin Seiple]
-
A.
Larkin Seiple
chosen
Larkin Seiple is an American cinematographer known for his visually inventive work on films such as "Everything Everywhere All at Once."
-
B.
Andrew Laeddis
Andrew Laeddis is the true identity of U.S. Marshal Teddy Daniels, revealed as a delusional patient in the psychological thriller "Shutter Island."
-
C.
John Luessenhop
John Luessenhop is an American film director and screenwriter best known for helming genre and action films, including the horror sequel "Texas Chainsaw 3D."
-
D.
Frank Doelger
Frank Doelger is a television producer best known for his work on the acclaimed HBO fantasy series "Game of Thrones."
-
E.
Christian Salyer
Christian Salyer is a composer and music producer known for creating scores and tracks for films and television, including the crime thriller "Catch .44."
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbbf0cec819084216807601afad1 |
completed | April 18, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170dd38708190b17cee0ab6cee6eb |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:29 a.m.