Triple
T14506482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penrose graphical notation |
E340276
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | tensor diagram formalism |
C28168
|
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: tensor diagram formalism Context triple: [Penrose graphical notation, instanceOf, tensor diagram formalism]
-
A.
tensor calculus
Tensor calculus is a branch of mathematics that generalizes vector calculus to tensors, providing coordinate-independent tools for analyzing multidimensional quantities and their transformations, especially in physics and differential geometry.
-
B.
framework for tensor analysis
chosen
A framework for tensor analysis is a structured system of concepts, operations, and tools that enables the representation, manipulation, and interpretation of multi-dimensional data using tensor algebra and related computational methods.
-
C.
tensor derivation
A tensor derivation is a linear map on a tensor algebra that satisfies the Leibniz rule, generalizing the notion of differentiation to tensor fields.
-
D.
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.
-
E.
tensor field
A tensor field is a mathematical object that assigns a tensor (a multilinear map or multidimensional array following specific transformation rules) to every point in a space or manifold, varying smoothly from point to point.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 10, 2026, 1:21 a.m.