Triple
T15961291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korteweg–De Vries equation |
E387064
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | nonlinear partial differential equation |
C3712
|
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: nonlinear partial differential equation Context triple: [Korteweg–De Vries equation, instanceOf, nonlinear partial differential equation]
-
A.
partial differential equation
chosen
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.
fully nonlinear equation
A fully nonlinear equation is a differential equation in which the highest-order derivatives appear in a genuinely nonlinear way, not just linearly or as coefficients of lower-order terms.
-
C.
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.
-
D.
nonlinear function
A nonlinear function is a mathematical relationship between variables in which the rate of change is not constant, so its graph does not form a straight line.
-
E.
variable-coefficient differential equation
A variable-coefficient differential equation is a differential equation in which the coefficients multiplying the unknown function and its derivatives depend on the independent variable(s) rather than being constant.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
Created at: April 10, 2026, 4:53 a.m.