Triple
T7985486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MapReduce |
E185673
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | parallel computing model |
C7185
|
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: parallel computing model Context triple: [MapReduce, instanceOf, parallel computing model]
-
A.
GPU computing framework
A GPU computing framework is a software platform that enables developers to write, manage, and optimize parallel programs that execute on graphics processing units for high-performance computation.
-
B.
model of computation
chosen
A model of computation is an abstract mathematical framework that defines how algorithms are represented and executed, specifying the rules, operations, and resources available for performing computations.
-
C.
distributed computing paper
A distributed computing paper is a scholarly work that presents theories, algorithms, systems, or empirical studies related to computation performed across multiple interconnected machines or processes.
-
D.
high-performance computing system
A high-performance computing system is an integrated collection of powerful processors, high-speed interconnects, and optimized software designed to perform large-scale, complex computations at very high speeds.
-
E.
GPU-accelerated application
A GPU-accelerated application is software that offloads compute-intensive tasks from the CPU to a graphics processing unit (GPU) to achieve significantly higher performance and parallel processing efficiency.
- 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
Created at: March 30, 2026, 5:15 p.m.