Triple
T17077739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coca-Cola red |
E414392
|
entity |
| Predicate | visuallyContrastsWith |
P76521
|
FINISHED |
| Object | white Coca-Cola script logo |
—
|
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: white Coca-Cola script logo | Statement: [Coca-Cola red, visuallyContrastsWith, white Coca-Cola script logo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visuallyContrastsWith Context triple: [Coca-Cola red, visuallyContrastsWith, white Coca-Cola script logo]
-
A.
providesContrastWith
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
B.
achievesContrast
chosen
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
C.
contrastRatio
Indicates the proportional difference in luminance or intensity between two visual elements being compared.
-
D.
createsContrastIn
Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
-
E.
dramaticContrastWith
Indicates that one entity is presented in a way that sharply emphasizes differences in tone, style, or impact when compared with 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbc625c48190b679a521180e10ad |
completed | April 18, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69e35d642f74819098c014135e249b27 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:34 a.m.