Triple
T8823869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NVIDIA Image Scaling |
E209965
|
entity |
| Predicate | sharpeningType |
P84823
|
FINISHED |
| Object | adaptive sharpening |
—
|
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: adaptive sharpening | Statement: [NVIDIA Image Scaling, sharpeningType, adaptive sharpening]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharpeningType Context triple: [NVIDIA Image Scaling, sharpeningType, adaptive sharpening]
-
A.
bladeType
Indicates the specific kind or category of blade associated with an object or entity.
-
B.
sharpnessCondition
Indicates the condition or degree of sharpness that something possesses or is required to have.
-
C.
bladeEdge
Indicates that one entity is the cutting edge or sharpened boundary of a blade-related object in relation to another entity.
-
D.
bladeDetail
Indicates that there is specific descriptive information or characteristics associated with a blade.
-
E.
cuttingStyle
Indicates the manner or technique in which something is cut or shaped.
- F. None of above. chosen
Provenance (4 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_69ca8364e13081909c85fe80f44fe86f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc6030b25081909d67488b35a72e05 |
completed | April 1, 2026, midnight |
| PD | Predicate disambiguation | batch_69cc5c21e64c81908490e3b0875dc0d6 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cff3608819081d2d7e5c16d44b7 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:46 p.m.