Triple
T17025852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedicab Driver |
E413060
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Arthur Wong |
—
|
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: Arthur Wong | Statement: [Pedicab Driver, cinematographyBy, Arthur Wong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arthur Wong Context triple: [Pedicab Driver, cinematographyBy, Arthur Wong]
-
A.
Arthur Wong
chosen
Arthur Wong is a renowned Hong Kong cinematographer known for his work on numerous action and martial arts films.
-
B.
Stephen Wong
Stephen Wong is a technology entrepreneur best known as a founder of the software company Embarcadero Technologies.
-
C.
Victor Wong
Victor Wong was an American character actor known for his distinctive presence in films such as "The Last Emperor," "Big Trouble in Little China," and "Tremors."
-
D.
Richard Wong
Richard Wong is a cinematographer and filmmaker known for his work on feature films such as "Snow Flower and the Secret Fan."
-
E.
Russell Wong
Russell Wong is an American actor and martial artist best known for his roles in action films and television series, often portraying skilled fighters or law enforcement characters.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
Created at: April 10, 2026, 5:33 a.m.