Triple
T17025687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wheels on Meals |
E413057
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Arthur Wong
Arthur Wong is a renowned Hong Kong cinematographer known for his work on numerous action and martial arts films.
|
E1276649
|
NE FINISHED |
How this triple was built (4 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: [Wheels on Meals, cinematographyBy, Arthur Wong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arthur Wong Context triple: [Wheels on Meals, cinematographyBy, Arthur Wong]
-
A.
Stephen Wong
Stephen Wong is a technology entrepreneur best known as a founder of the software company Embarcadero Technologies.
-
B.
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."
-
C.
Richard Wong
Richard Wong is a cinematographer and filmmaker known for his work on feature films such as "Snow Flower and the Secret Fan."
-
D.
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.
-
E.
Peter Kwong
Peter Kwong is an American character actor best known for his roles in genre films and television, including cult favorites from the 1980s and 1990s.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Arthur Wong Triple: [Wheels on Meals, cinematographyBy, Arthur Wong]
Generated description
Arthur Wong is a renowned Hong Kong cinematographer known for his work on numerous action and martial arts films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arthur Wong Target entity description: Arthur Wong is a renowned Hong Kong cinematographer known for his work on numerous action and martial arts films.
-
A.
Stephen Wong
Stephen Wong is a technology entrepreneur best known as a founder of the software company Embarcadero Technologies.
-
B.
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."
-
C.
Richard Wong
Richard Wong is a cinematographer and filmmaker known for his work on feature films such as "Snow Flower and the Secret Fan."
-
D.
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.
-
E.
Peter Kwong
Peter Kwong is an American character actor best known for his roles in genre films and television, including cult favorites from the 1980s and 1990s.
- F. None of above. chosen
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01ddcf76fc81908b455c570e12d4d8 |
completed | May 11, 2026, 1:46 p.m. |
| NEDg | Description generation | batch_6a01df83eae88190b8203169c73affb1 |
completed | May 11, 2026, 1:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01dfbfc47881908fc933be12de619d |
completed | May 11, 2026, 1:55 p.m. |
Created at: April 10, 2026, 5:33 a.m.