Triple
T28901601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ARM SVE |
E732964
|
entity |
| Predicate | vectorLengthGranularityBits |
P137183
|
FINISHED |
| Object | 128 |
—
|
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: 128 | Statement: [ARM SVE, vectorLengthGranularityBits, 128]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vectorLengthGranularityBits Context triple: [ARM SVE, vectorLengthGranularityBits, 128]
-
A.
vectorLength
Indicates the numerical magnitude or size of a vector, typically computed from its components.
-
B.
grainSize
Indicates the relative coarseness or fineness of the material or particles involved in the relationship.
-
C.
scalingGranularity
chosen
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
D.
hasMinimalVectorLength
Indicates that the associated vector has a length (magnitude) that meets or exceeds a specified minimal threshold.
-
E.
granularityLevel
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
- 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_69f05b08c2008190ac426a035a2ed66d |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f65b14512c8190a40e70319dcc54cd |
completed | May 2, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f659d02f1c8190831758ac52bb54e4 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 8:03 a.m.