Triple
T16056488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tusk |
E389493
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | James Laxton |
E355739
|
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: James Laxton | Statement: [Tusk, cinematographyBy, James Laxton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: James Laxton Context triple: [Tusk, cinematographyBy, James Laxton]
-
A.
James Laxton
chosen
James Laxton is an American cinematographer best known for his acclaimed, visually distinctive work on the Oscar-winning film "Moonlight."
-
B.
Richard Laxton
Richard Laxton is a British film and television director known for his work on dramas such as the period film "Effie Gray."
-
C.
Robert Lence
Robert Lence is an American screenwriter and story artist best known for his work on acclaimed animated films such as Disney’s "Beauty and the Beast."
-
D.
Philip Latham
Philip Latham was a British actor best known for his character roles in film and television, particularly in period dramas.
-
E.
Paul Broughton
Paul Broughton is an actor best known for his role in the British television drama series "The Lakes."
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1837579488190964ca004c2eb01c4 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb8b1b2c8190949943b20f2f8574 |
completed | May 10, 2026, 2:20 a.m. |
Created at: April 10, 2026, 4:56 a.m.