Triple
T16852904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hisense (selected models) |
E409717
|
entity |
| Predicate | colorReproduction |
P99568
|
FINISHED |
| Object | wide color gamut |
—
|
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: wide color gamut | Statement: [Hisense (selected models), colorReproduction, wide color gamut]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorReproduction Context triple: [Hisense (selected models), colorReproduction, wide color gamut]
-
A.
colorImaging
Indicates a relationship where an entity captures, processes, or represents visual information specifically in terms of color.
-
B.
hasBetterColorReproductionThan
Indicates that one entity produces more accurate or higher-quality color representation than another entity.
-
C.
coversColorSpace
chosen
Indicates that one entity’s color representation range fully includes or spans the color space defined by another entity.
-
D.
color work
Indicates that an entity applies or adds color to another entity, typically as part of a creative or finishing process.
-
E.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b37abadc81909d02d329403497d6 |
completed | April 18, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.