Triple
T28615925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VirtualFree |
E724271
|
entity |
| Predicate | memoryGranularity |
P166493
|
FINISHED |
| Object | page granularity |
—
|
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: page granularity | Statement: [VirtualFree, memoryGranularity, page granularity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memoryGranularity Context triple: [VirtualFree, memoryGranularity, page granularity]
-
A.
allocationGranularity
chosen
Indicates the size or unit in which a resource or memory region is divided and assigned during allocation.
-
B.
scalingGranularity
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
C.
memoryManagementUnit
Indicates a relationship where a component or mechanism manages and translates memory addresses or access between different parts of a system.
-
D.
grainSize
Indicates the relative coarseness or fineness of the material or particles involved in the relationship.
-
E.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
- 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_69f01d816d7c8190a1fe27e3434041dc |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f67f0488bc819089fbd2d2478158d3 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 28, 2026, 4:31 a.m.