Triple
T14573111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glacier |
E341969
|
entity |
| Predicate | visualImpression |
P25983
|
FINISHED |
| Object | light grayish cool color |
—
|
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: light grayish cool color | Statement: [Glacier, visualImpression, light grayish cool color]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualImpression Context triple: [Glacier, visualImpression, light grayish cool color]
-
A.
visualFeature
chosen
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
B.
usesImagery
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
-
C.
evokesImageOf
Indicates that one entity triggers or brings to mind a mental image or visual representation of another entity.
-
D.
visualMetaphor
Indicates a relationship where one entity conceptually represents or explains another through a visual analogy or symbolic imagery.
-
E.
visualMedium
Indicates that one entity serves as the visual medium or format through which another entity is presented, communicated, or experienced.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f33b1c8190bb447788bfd28d51 |
completed | April 14, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69de656a953481909a4645b004c40de7 |
completed | April 14, 2026, 4:03 p.m. |
Created at: April 10, 2026, 1:24 a.m.