Triple
T32807708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CSR (Compressed Sparse Row) |
E839061
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | sparse matrix storage format |
C39522
|
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 storage format Context triple: [CSR (Compressed Sparse Row), instanceOf, sparse matrix storage format]
-
A.
sparse matrix type
chosen
A sparse matrix type is a data structure that efficiently stores and manipulates matrices with predominantly zero elements by recording only the nonzero entries and their positions.
-
B.
structured matrix
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.
-
C.
array-oriented data format
An array-oriented data format is a structured way of organizing and storing data primarily as multidimensional arrays, enabling efficient numerical computation, slicing, and bulk operations.
-
D.
row-oriented storage format
A row-oriented storage format is a data layout where all the column values for each record are stored together contiguously, optimizing for transactional workloads that frequently read or write entire rows.
-
E.
lossless compression format
A lossless compression format is a method of encoding data that reduces file size without discarding any information, allowing the original data to be perfectly reconstructed upon decompression.
- 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_69f3493d35208190b4351b4e85f2fa16 |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:15 a.m.