Triple
T1748909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cats (2019 film) |
E38394
|
entity |
| Predicate | featuresVisualEffects |
P16366
|
FINISHED |
| Object | extensive CGI fur technology |
—
|
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: extensive CGI fur technology | Statement: [Cats (2019 film), featuresVisualEffects, extensive CGI fur technology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresVisualEffects Context triple: [Cats (2019 film), featuresVisualEffects, extensive CGI fur technology]
-
A.
visualEffect
chosen
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
B.
specialEffectsBy
Indicates that the special effects for something (such as a film, scene, or shot) are created or provided by a particular person or entity.
-
C.
featuresReimaginedVersionOf
Indicates that something includes or presents a newly interpreted or updated version of another existing work or element.
-
D.
featuresStyle
Indicates that one entity exhibits, incorporates, or is characterized by a particular style associated with another entity.
-
E.
videoModulation
Indicates a relationship where one entity alters or controls the characteristics of a video signal or stream, such as its amplitude, frequency, or encoding parameters.
- 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab630e7d008190a8c673665d9672bb |
completed | March 6, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69aa61c5a18481909bc49e0c54d64314 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.