Triple
T32174284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | An Autumn's Tale |
E821793
|
entity |
| Predicate | originatesFromFilmIndustry |
P101250
|
FINISHED |
| Object | Hong Kong film industry |
—
|
LITERAL 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: Hong Kong film industry | Statement: [An Autumn's Tale, originatesFromFilmIndustry, Hong Kong film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originatesFromFilmIndustry Context triple: [An Autumn's Tale, originatesFromFilmIndustry, Hong Kong film industry]
-
A.
originatesInFilmIndustry
chosen
Indicates that something has its source, development, or primary origin within the film industry.
-
B.
sangForFilmIndustry
Indicates that a person performed singing specifically for use in the film industry, such as in movies or film soundtracks.
-
C.
hasFilmIndustryCenter
Indicates that a location serves as a primary hub or central base for activities related to the film industry.
-
D.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
-
E.
isFromFilm
Indicates that something (such as a quote, character, scene, or element) originates from or appears in a particular film.
- 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_69f3490699a48190bbef96b198e8fade |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a017114e7148190b7479284c316443d |
completed | May 11, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_6a016eebd60481909212e2a49ef95fbe |
completed | May 11, 2026, 5:53 a.m. |
Created at: May 1, 2026, 12:34 a.m.