Triple
T9026916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam440ep-flex |
E216069
|
entity |
| Predicate | supports2DGraphicsAcceleration |
P85941
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Sam440ep-flex, supports2DGraphicsAcceleration, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supports2DGraphicsAcceleration Context triple: [Sam440ep-flex, supports2DGraphicsAcceleration, true]
-
A.
supports3DAcceleration
Indicates that one entity provides or enables hardware- or software-based 3D graphics acceleration for another entity.
-
B.
supportsHardwareAcceleration
Indicates that one entity enables or provides hardware-based acceleration capabilities for another entity’s operations or processes.
-
C.
hardwareAcceleration
Indicates that an operation or process is executed using specialized hardware resources (such as GPU or dedicated accelerators) rather than relying solely on general-purpose CPU computation.
-
D.
supportsNeuralNetworkAcceleration
Indicates that one entity provides hardware or software capabilities that enhance the speed or efficiency of neural network computations for another entity.
-
E.
supportsOffscreenRendering
Indicates that the subject is capable of performing rendering operations to an offscreen buffer or surface rather than directly to the visible display.
- 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_69ca83a5fa88819088144801b4dd7245 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6a7eb5b881908ace0c3327f06161 |
completed | April 1, 2026, 12:44 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee132f08190940749c7c522e4c1 |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc5f887108819096d45a186fc137b3 |
completed | March 31, 2026, 11:58 p.m. |
Created at: March 30, 2026, 7:07 p.m.