Triple
T5167681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omni-Path |
E116598
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | high-performance computing interconnect technology |
C2279
|
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: high-performance computing interconnect technology Context triple: [Omni-Path, instanceOf, high-performance computing interconnect technology]
-
A.
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.
-
B.
network technology
chosen
Network technology encompasses the hardware, software, and protocols that enable devices and systems to connect, communicate, and exchange data over local and wide-area networks, including the internet.
-
C.
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.
-
D.
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.
-
E.
GPU architecture
GPU architecture is the conceptual design and organization of a graphics processing unit’s cores, memory hierarchy, and data paths that enable massively parallel computation for graphics and general-purpose workloads.
- 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_69bd445ff97c81909a2615cc56235470 |
completed | March 20, 2026, 12:58 p.m. |
Created at: March 20, 2026, 1:45 p.m.