Triple
T27798972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FlumeJava |
E702188
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | data-parallel programming framework |
C11253
|
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-parallel programming framework Context triple: [FlumeJava, instanceOf, data-parallel programming framework]
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
big data framework
chosen
A big data framework is a software platform that enables the distributed storage, processing, and analysis of large-scale, complex datasets across clusters of machines.
-
E.
GPU-accelerated array library
A GPU-accelerated array library is a software toolkit that provides high-level, NumPy-like array operations executed on graphics processing units to enable massively parallel, high-performance numerical computing.
- 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_69ef8408e0588190977cffa32dc33a29 |
completed | April 27, 2026, 3:43 p.m. |
Created at: April 27, 2026, 5:33 p.m.