Triple
T7388222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goya inference processor |
E170435
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | inference processor |
C8436
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: inference processor Context triple: [Goya inference processor, instanceOf, inference processor]
-
A.
hardware accelerator
chosen
A hardware accelerator is a specialized computing device or component designed to perform specific tasks or algorithms more efficiently and faster than a general-purpose processor.
-
B.
hardware accelerator integration
Hardware accelerator integration is the process of connecting and coordinating specialized processing units (such as GPUs, TPUs, or FPGAs) with a computing system’s hardware and software stack to offload and speed up specific computational tasks.
-
C.
microprocessor
A microprocessor is a compact, integrated circuit that performs the arithmetic, logic, control, and input/output operations of a computer’s central processing unit (CPU) on a single chip.
-
D.
graphics processing unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly perform parallel mathematical and geometric calculations to render images, videos, and visual effects for display.
-
E.
SIMD instruction set extension
A SIMD instruction set extension is a set of processor instructions that enable performing the same operation simultaneously on multiple data elements to accelerate parallelizable computations.
- F. None of above.
Provenance (1 batch)
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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
Created at: March 27, 2026, 3:09 p.m.