Triple
T17676541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BlockBandedMatrix |
E440655
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | sparse matrix representation |
C26958
|
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: sparse matrix representation Context triple: [BlockBandedMatrix, instanceOf, sparse matrix representation]
-
A.
structured matrix
chosen
A structured matrix is a matrix whose entries follow a specific pattern or rule (such as Toeplitz, circulant, or banded structure), enabling more efficient storage and computation than a general dense matrix.
-
B.
decentralized field representation
A decentralized field representation is a way of encoding a spatially or temporally varying quantity using many local, distributed parameters or agents, rather than a single centralized model, so that the overall field emerges from their collective behavior.
-
C.
two-dimensional representation
A two-dimensional representation is a mapping of abstract elements or data into a flat plane using two axes or coordinates, enabling visualization and analysis of relationships in two spatial dimensions.
-
D.
mathematical representation
A mathematical representation is a formal, structured way of expressing abstract mathematical objects or relationships—such as numbers, functions, or systems—using symbols, equations, diagrams, or other mathematical constructs to enable analysis and reasoning.
-
E.
tensor
A tensor is a multidimensional array of numerical values that generalizes scalars, vectors, and matrices to represent data or linear relationships across multiple dimensions.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
Created at: April 10, 2026, 10:01 a.m.