Triple
T10845133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I, Frankenstein |
E255989
|
entity |
| Predicate | usesVisualEffects |
P16366
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [I, Frankenstein, usesVisualEffects, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesVisualEffects Context triple: [I, Frankenstein, usesVisualEffects, yes]
-
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.
hasLightingEffect
Indicates that one entity applies, produces, or is associated with a particular lighting effect on another entity or environment.
-
C.
hasVisualEffectsSupervisor
Indicates that one entity serves as the visual effects supervisor responsible for overseeing the visual effects work on another entity (such as a film, episode, or production).
-
D.
transparencyEffects
Indicates how the level or presence of transparency in one entity influences the perception, behavior, or properties of another entity.
-
E.
providesSensoryEffects
Indicates that one entity causes or contributes to sensory experiences or perceptions in another entity.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d750d0155c81908fb55ba6b45db800 |
completed | April 9, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69d70d2b51448190bae748ed6c23edde |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:19 p.m.