Triple
T5514637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinda Coral |
E144650
|
entity |
| Predicate | colorTemperature |
P27165
|
FINISHED |
| Object | warm |
—
|
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: warm | Statement: [Kinda Coral, colorTemperature, warm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorTemperature Context triple: [Kinda Coral, colorTemperature, warm]
-
A.
whitePointCCT
Indicates the correlated color temperature value that defines the white point reference for a color space or imaging system.
-
B.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
C.
hasEffectiveTemperature
Indicates that an entity (typically a star or other astronomical object) possesses a specific effective surface temperature characterizing its emitted radiation.
-
D.
lightingColor
chosen
Indicates the color or hue of the lighting applied to or associated with an entity.
-
E.
whitePoint
Indicates the reference color point or standard white used as a basis for color measurements or calibration in a color space.
- 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_69c008f77ff88190b0cd50ca207295d1 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f5b4e988190b590b4157cf089c1 |
completed | March 22, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69c01b0a06348190b39ac9fe80d2836a |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:33 p.m.