Triple
T17676207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UpperTriangular |
E440648
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | linear algebra data structure |
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: linear algebra data structure Context triple: [UpperTriangular, instanceOf, linear algebra data structure]
-
A.
result in linear algebra
In linear algebra, a result is a proven statement or conclusion—such as a theorem, lemma, or corollary—that follows logically from definitions and previously established facts about vectors, matrices, and linear transformations.
-
B.
numerical linear algebra library
A numerical linear algebra library is a collection of optimized routines and data structures for performing matrix and vector operations, decompositions, and related numerical computations.
-
C.
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.
-
D.
vector space
A vector space is a set of objects called vectors, equipped with operations of vector addition and scalar multiplication that satisfy specific axioms such as associativity, commutativity, distributivity, and the existence of additive identities and inverses.
-
E.
mathematical structure
A mathematical structure is a set (or collection of objects) equipped with specified operations, relations, or properties that satisfy given axioms, providing a framework for studying abstract patterns and relationships.
- 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 a.m.