Triple
T275987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Demoiselles d'Avignon |
E5250
|
entity |
| Predicate | colorCharacteristic |
P60
|
FINISHED |
| Object | dominant pink and blue tones |
—
|
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: dominant pink and blue tones | Statement: [Les Demoiselles d'Avignon, colorCharacteristic, dominant pink and blue tones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorCharacteristic Context triple: [Les Demoiselles d'Avignon, colorCharacteristic, dominant pink and blue tones]
-
A.
colors
chosen
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
B.
characterizedBy
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
C.
colorCharge
Indicates a relationship where an entity possesses a specific quantum color charge (such as red, green, or blue) in the context of strong nuclear interactions.
-
D.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
E.
colorOftenUsed
Indicates that a particular color is frequently used or commonly applied in relation to something.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25dec53ac8190912f3d79576131fa |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b7480e881909399beccfc7ffb81 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.