Triple
T38310011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CrossFire |
E1033657
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | multi-GPU technology |
C55701
|
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: multi-GPU technology Context triple: [CrossFire, instanceOf, multi-GPU technology]
-
A.
NVIDIA technology
NVIDIA technology encompasses a range of advanced hardware and software solutions—most notably GPUs, AI platforms, and high-performance computing systems—designed to accelerate graphics, data processing, and machine learning workloads across industries.
-
B.
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.
-
C.
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.
-
D.
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.
-
E.
graphics acceleration technology
chosen
Graphics acceleration technology is specialized hardware and software that offloads and speeds up the processing of visual and graphical computations, enabling smoother rendering and higher performance for images, videos, and 3D applications.
- 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_69f76e132c408190969b3d35c04b87ae |
completed | May 3, 2026, 3:47 p.m. |
Created at: May 3, 2026, 4:30 p.m.