Triple
T35836740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One Nite in Mongkok |
E1035958
|
entity |
| Predicate | policeCharacterPortrayedBy |
P1507
|
FINISHED |
| Object | Alex Fong Chung-sun |
—
|
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: Alex Fong Chung-sun | Statement: [One Nite in Mongkok, policeCharacterPortrayedBy, Alex Fong Chung-sun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policeCharacterPortrayedBy Context triple: [One Nite in Mongkok, policeCharacterPortrayedBy, Alex Fong Chung-sun]
-
A.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
B.
portrayedDetective
Indicates that one entity has played or depicted a detective character in a performance or work.
-
C.
accusedCharacterPortrayedBy
Indicates that a particular actor or performer plays the role of the character who is accused within a given work.
-
D.
portrayedBy
chosen
Indicates that one entity serves as the actor or performer who represents or plays the role of another entity in a work or medium.
-
E.
leadActorForCharacter Officer Otis
Indicates that the specified person is the primary actor portraying the character Officer Otis.
- F. None of above.
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_69f76e192a94819082db360cb91e6a8d |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcc7338120819081cb46547d60f2cb |
completed | May 7, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
Created at: May 3, 2026, 4:06 p.m.