Triple
T11099087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clebsch representation |
E262456
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | vector field representation |
C29202
|
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: vector field representation Context triple: [Clebsch representation, instanceOf, vector field representation]
-
A.
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.
-
B.
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.
-
C.
vector graphics editor
A vector graphics editor is a software application used to create and manipulate images composed of scalable geometric shapes such as lines, curves, and polygons, allowing for resolution-independent artwork and precise design control.
-
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.
vector graphics file format
A vector graphics file format is a digital image format that stores pictures as scalable mathematical descriptions of shapes, lines, and colors rather than as fixed pixels.
- F. None of above. chosen
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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:27 p.m.