Triple
T34935459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unending Blues |
E1007556
|
entity |
| Predicate | usesImageryType |
P64074
|
FINISHED |
| Object | surreal imagery |
—
|
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: surreal imagery | Statement: [Unending Blues, usesImageryType, surreal imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesImageryType Context triple: [Unending Blues, usesImageryType, surreal imagery]
-
A.
usesImagery
chosen
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
-
B.
usesImageryOf
Indicates that one entity employs or incorporates visual or sensory imagery that depicts, references, or symbolically represents another entity.
-
C.
hasColorImagery
Indicates that something includes or is characterized by visual elements emphasizing specific colors or color-based symbolism.
-
D.
hasImageryType
Indicates that one entity is associated with a specific kind or category of imagery (such as visual style, medium, or representation type) used to depict or describe it.
-
E.
usesImageModel
Indicates that one entity employs or relies on an image-based model (such as a computer vision or image generation model) in relation to another entity or task.
- 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_69f76dc513fc819084a1ff52abbfa5bc |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.