Triple
T4436341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madonna of the Chair |
E95658
|
entity |
| Predicate | colorCharacteristics |
P60
|
FINISHED |
| Object | rich warm 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: rich warm tones | Statement: [Madonna of the Chair, colorCharacteristics, rich warm tones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorCharacteristics Context triple: [Madonna of the Chair, colorCharacteristics, rich warm tones]
-
A.
colors
chosen
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
B.
lightingCharacteristic
Indicates the specific qualities or properties of how something is lit, such as brightness, color, direction, or style of illumination.
-
C.
typicalColorDescription
Indicates the usual or characteristic color associated with an entity.
-
D.
colorVarietyOf
Indicates that one entity represents a specific color variant or color option of another entity.
-
E.
colorTheory
Indicates a relationship where principles or concepts about how colors interact, combine, or affect perception are applied or referenced between entities.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35589f8608190b0820d36beaacf44 |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:31 p.m.