Triple
T37712597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master of the World |
E939378
|
entity |
| Predicate | hasVisualEffectType |
P16366
|
FINISHED |
| Object | miniature effects |
—
|
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: miniature effects | Statement: [Master of the World, hasVisualEffectType, miniature effects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisualEffectType Context triple: [Master of the World, hasVisualEffectType, miniature effects]
-
A.
hasTypeOfEffect
Indicates that one entity produces, exhibits, or is associated with a particular kind or category of effect on another entity or context.
-
B.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
C.
visualEffect
chosen
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
D.
hasVisuals
Indicates that one entity includes, displays, or is associated with visual elements or imagery related to another entity.
-
E.
hasFrameEffect
Indicates that one entity produces or is associated with a visual or stylistic frame-related effect on 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_69f76edb49dc8190b951dce9ce6ef789 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdbaa226708190b8ed96e93aad38de |
completed | May 8, 2026, 10:27 a.m. |
| PD | Predicate disambiguation | batch_69fdb58b07e48190837e00966de050d4 |
completed | May 8, 2026, 10:06 a.m. |
Created at: May 3, 2026, 4:18 p.m.