Triple
T11958444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Open Multi-Processing |
E284609
|
entity |
| Predicate | parallelizationGranularity |
P15041
|
FINISHED |
| Object | loop-level parallelism |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: loop-level parallelism | Statement: [Open Multi-Processing, parallelizationGranularity, loop-level parallelism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: parallelizationGranularity Context triple: [Open Multi-Processing, parallelizationGranularity, loop-level parallelism]
-
A.
isParallelizable
Indicates that a process, task, or operation can be decomposed into independent parts that may be executed concurrently without affecting correctness.
-
B.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
-
C.
parallelCountBy
Indicates performing a counting or aggregation operation over items in parallel, grouping results by a specified key or category.
-
D.
grainSize
Indicates the relative coarseness or fineness of the material or particles involved in the relationship.
-
E.
parallelSubdivision
chosen
Indicates that one structure or process is divided into multiple parts that proceed or are handled simultaneously alongside each other.
- F. None of above.
Provenance (3 batches)
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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903681a00819098c2b5260e2ef834 |
completed | April 10, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.