Triple
T8414857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NEON SIMD |
E198707
|
entity |
| Predicate | vectorLength |
P82041
|
FINISHED |
| Object | 64-bit vector |
—
|
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: 64-bit vector | Statement: [NEON SIMD, vectorLength, 64-bit vector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vectorLength Context triple: [NEON SIMD, vectorLength, 64-bit vector]
-
A.
vector
Indicates that one entity is a vector associated with, representing, or characterizing another entity (such as a quantity with magnitude and direction, or a carrier/representative of something).
-
B.
dimensionVector
Indicates a vector that specifies the magnitudes or extents of an entity along multiple dimensions or measurement axes.
-
C.
basisVectorsCount
Indicates the number of basis vectors associated with a given vector space or basis.
-
D.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
E.
dimensionCount
Indicates the number of distinct dimensions or axes associated with an entity or data structure.
- F. None of above. chosen
Provenance (4 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e443a08190983d9a0a61e0f781 |
completed | March 31, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69cb70d70ea081909c3dc1bd2ec14f85 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb77690720819099de1e22b84a9563 |
completed | March 31, 2026, 7:27 a.m. |
Created at: March 30, 2026, 6:06 p.m.