Triple
T28582819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIMD within a register (SWAR) |
E723422
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | data-level parallelism technique |
C54278
|
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: data-level parallelism technique Context triple: [SIMD within a register (SWAR), instanceOf, data-level parallelism technique]
-
A.
data-parallel execution engine
A data-parallel execution engine is a system that coordinates the simultaneous processing of independent data partitions across multiple compute resources to accelerate large-scale computations.
-
B.
independent parallel section
An independent parallel section is a self-contained, concurrently executing part of a system or process that runs alongside others without direct interdependencies, enabling parallel progress and simplified coordination.
-
C.
parallel programming library
A parallel programming library is a collection of tools, abstractions, and APIs that enable developers to write programs that execute multiple computations concurrently across multiple cores, processors, or machines to improve performance and scalability.
-
D.
parallel computing standard
A parallel computing standard is a formally defined specification that enables coordinated execution and communication among multiple processing elements to efficiently perform computations concurrently across diverse hardware platforms.
-
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. chosen
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_69f01d7f92e481909847f5f3f3174a89 |
completed | April 28, 2026, 2:37 a.m. |
Created at: April 28, 2026, 4:16 a.m.