Triple
T30405045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Synergistic Processing Element |
E773455
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | SIMD processing element |
C8847
|
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: SIMD processing element Context triple: [Synergistic Processing Element, instanceOf, SIMD processing element]
-
A.
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.
-
B.
vector processing extension
chosen
A vector processing extension is a hardware or software enhancement to a processor’s instruction set that enables efficient parallel operations on multiple data elements within single instructions, improving performance for data-intensive workloads.
-
C.
hardware accelerator
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.
-
D.
Intelligence Processing Unit
An Intelligence Processing Unit is a specialized computing component designed to efficiently execute and accelerate artificial intelligence and machine learning workloads through massively parallel, low-precision, and dataflow-oriented architectures.
-
E.
simultaneous multithreading technology
Simultaneous multithreading technology is a processor design technique that allows multiple independent instruction threads to be issued and executed in the same clock cycle on a single physical core, improving utilization of execution resources and overall throughput.
- 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_69f2248facd48190b183c3f3ca6daef7 |
completed | April 29, 2026, 3:32 p.m. |
Created at: April 29, 2026, 8:03 p.m.