Triple
T21826249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gipsy Danger in Hong Kong |
E538861
|
entity |
| Predicate | hasCinematicFeature |
P145815
|
FINISHED |
| Object | heavyUseOfPracticalEffectsAndCGI |
—
|
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: heavyUseOfPracticalEffectsAndCGI | Statement: [Gipsy Danger in Hong Kong, hasCinematicFeature, heavyUseOfPracticalEffectsAndCGI]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCinematicFeature Context triple: [Gipsy Danger in Hong Kong, hasCinematicFeature, heavyUseOfPracticalEffectsAndCGI]
-
A.
hasCinematicShort
Indicates that an entity is associated with or includes a cinematic short film or short-form cinematic content.
-
B.
hasCinematicThemes
Indicates that something incorporates or is characterized by themes, motifs, or stylistic elements commonly associated with cinema or film.
-
C.
supportsCinematicMode
Indicates that one entity provides or enables a cinematic mode feature for another entity.
-
D.
cinematicForm
Indicates that something is expressed, structured, or realized through the techniques, conventions, or medium of cinema or film.
-
E.
hasInteractiveFilm
Indicates that an entity is associated with, offers, or features an interactive film experience.
- 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_69e0c475038c8190abb9b1a20eb8ff50 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f09132ae888190b8c1a8e75b96b5fd |
completed | April 28, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69e6be815a108190be81d7c987d0c0d6 |
completed | April 21, 2026, 12:02 a.m. |
| PDg | Predicate description generation | batch_69e6c670ee608190b9cfdc09de74f0de |
completed | April 21, 2026, 12:36 a.m. |
Created at: April 16, 2026, 6:54 p.m.