Triple
T17561350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Beam |
E427701
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | unified programming 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: unified programming model Context triple: [Apache Beam, instanceOf, unified programming model]
-
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.
directive-based programming model
A directive-based programming model is a high-level parallel programming approach where developers annotate code with compiler-interpreted directives (pragmas) to express parallelism and data movement without explicitly managing low-level threading or synchronization details.
-
C.
computing platform ecosystem
A computing platform ecosystem is an interconnected environment of hardware, software, services, and stakeholders that collectively enable the development, distribution, and use of applications on a shared technological foundation.
-
D.
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.
-
E.
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.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:50 a.m.