Triple
T32439695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Better Tomorrow II |
E828978
|
entity |
| Predicate | ratingHongKong |
P174088
|
FINISHED |
| Object | Category III |
—
|
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: Category III | Statement: [A Better Tomorrow II, ratingHongKong, Category III]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ratingHongKong Context triple: [A Better Tomorrow II, ratingHongKong, Category III]
-
A.
releaseDateHongKong
Indicates the date on which something (such as a product, film, or work) was officially released in Hong Kong.
-
B.
releaseDateInHongKong
Indicates the date on which something is officially released or made available in Hong Kong.
-
C.
filmRatingKorea
Indicates that a film has a specific official content rating assigned by the Korean rating authority.
-
D.
ratingOfWork
Indicates the evaluative score or assessment assigned to a particular work or creation.
-
E.
ratingCategory
Indicates the qualitative classification or level assigned to a rating (e.g., low, medium, high) within an evaluation or scoring system.
- F. None of above. chosen
Provenance (4 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_69f3491bf298819097b610f772d54a6d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c2e1a834819094e117bf9b6b3c15 |
completed | May 3, 2026, 3:37 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
| PDg | Predicate description generation | batch_69f6bb344bb48190a8089f29c0063ded |
completed | May 3, 2026, 3:04 a.m. |
Created at: May 1, 2026, 12:55 a.m.