Triple
T36828441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BlueGene/L |
E910071
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | massively parallel system |
C12874
|
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: massively parallel system Context triple: [BlueGene/L, instanceOf, massively parallel system]
-
A.
high-performance computing system
chosen
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.
-
B.
petascale supercomputer
A petascale supercomputer is a massively parallel high-performance computing system capable of performing at least one quadrillion (10^15) floating-point operations per second, used for large-scale scientific, engineering, and data-intensive simulations.
-
C.
parallel computing technique
A parallel computing technique is a method for dividing a computational task into smaller subtasks that can be executed simultaneously across multiple processors or cores to improve performance and efficiency.
-
D.
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.
-
E.
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.
- 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_69f76e7e9d60819092442fba73290a46 |
completed | May 3, 2026, 3:49 p.m. |
Created at: May 3, 2026, 4:13 p.m.