Triple
T9843885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cauchy–Euler equation |
E239291
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | variable-coefficient differential equation |
C26956
|
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: variable-coefficient differential equation Context triple: [Cauchy–Euler equation, instanceOf, variable-coefficient differential equation]
-
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.
integro-differential equations
Integro-differential equations are mathematical equations that involve both integrals and derivatives of an unknown function, capturing systems where current rates of change depend on accumulated past behavior.
-
C.
evolution equation
An evolution equation is a mathematical expression, typically a differential or integral equation, that describes how a system’s state changes over time according to specified dynamical rules.
-
D.
equation in the calculus of variations
An equation in the calculus of variations is a mathematical relation, typically an Euler–Lagrange equation, that characterizes the functions making a given functional stationary (usually minimizing or maximizing its value).
-
E.
linear differential operator
A linear differential operator is a mapping that takes a function as input and returns a new function formed by a linear combination of the function and its derivatives.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
Created at: March 30, 2026, 8:33 p.m.