Triple
T12889930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benson Fong |
E308331
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Benson Fong |
E308331
|
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: Benson Fong | Statement: [Benson Fong, name, Benson Fong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benson Fong Context triple: [Benson Fong, name, Benson Fong]
-
A.
Benson Fong
chosen
Benson Fong was an American character actor best known for his roles in mid-20th-century Hollywood films, including several entries in the Charlie Chan series.
-
B.
Larry Fong
Larry Fong is an American cinematographer known for his visually striking work on major films such as "300," "Watchmen," and "Batman v Superman: Dawn of Justice."
-
C.
Cato Fong
Cato Fong is Inspector Clouseau’s loyal but overzealous martial-arts-trained manservant in the Pink Panther film series, known for his surprise attacks meant to keep the bumbling detective alert.
-
D.
Johnny Chiang
Johnny Chiang is a Taiwanese politician who has served as a prominent leader within the Kuomintang (KMT) party and as a legislator in Taiwan’s Legislative Yuan.
-
E.
Ken Kao
Ken Kao is an American film producer known for backing a range of independent and auteur-driven projects.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714581988190afc720ffd7797860 |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a5598ad08190bad57ccfb4e4e2b6 |
completed | May 3, 2026, 1:31 a.m. |
Created at: April 9, 2026, 5:39 p.m.