Triple
T35454469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis Ducos du Hauron |
E1024731
|
entity |
| Predicate | usedColorModel |
P52856
|
FINISHED |
| Object | red-green-blue separation |
—
|
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: red-green-blue separation | Statement: [Louis Ducos du Hauron, usedColorModel, red-green-blue separation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedColorModel Context triple: [Louis Ducos du Hauron, usedColorModel, red-green-blue separation]
-
A.
hasColorModel
chosen
Indicates that an entity uses or is associated with a particular color representation model (such as RGB, CMYK, or HSV) for defining its colors.
-
B.
colorUse
Indicates that one entity uses, applies, or is associated with a particular color in its appearance, design, or representation.
-
C.
coversColorSpace
Indicates that one entity’s color representation range fully includes or spans the color space defined by another entity.
-
D.
usesColorMatchingFunctions
Indicates that one entity applies color matching functions to relate or convert color stimuli between different color spaces or representations.
-
E.
usedUniformColor
Indicates that multiple entities share or employed the same uniform color in a given context.
- 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_69f76df92f108190817222e520e22268 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:04 p.m.