Triple
T23801657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gagliardo–Nirenberg interpolation inequalities |
E588689
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | tool in partial differential equations |
C42144
|
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: tool in partial differential equations Context triple: [Gagliardo–Nirenberg interpolation inequalities, instanceOf, tool in partial differential equations]
-
A.
partial differential equation
A partial differential equation is an equation that relates the partial derivatives of an unknown multivariable function, describing how it changes with respect to several independent variables.
-
B.
result in partial differential equations
A result in partial differential equations is a proven statement or theorem that characterizes the existence, uniqueness, regularity, behavior, or qualitative properties of solutions to equations involving multivariable derivatives.
-
C.
tool in geometric analysis
A tool in geometric analysis is a mathematical method, concept, or construction used to study and characterize the geometric and analytic properties of spaces, shapes, and mappings.
-
D.
result in mathematical physics
A result in mathematical physics is a rigorously proven statement that connects precise mathematical structures with physical theories, often clarifying, justifying, or predicting phenomena within a formal framework.
-
E.
tool in approximation theory
chosen
A tool in approximation theory is a mathematical method, theorem, or construct used to analyze, measure, or improve how well functions or data can be approximated by simpler or more tractable representations.
- 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_69e25d15db58819092ac1e6791696fd9 |
completed | April 17, 2026, 4:17 p.m. |
Created at: April 17, 2026, 7:53 p.m.