Triple
T17040860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | theta-method |
E413442
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | finite difference time discretization method |
C7231
|
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: finite difference time discretization method Context triple: [theta-method, instanceOf, finite difference time discretization method]
-
A.
time-stepping scheme
chosen
A time-stepping scheme is a numerical method that advances the solution of time-dependent equations from one discrete time level to the next.
-
B.
numerical integration method for ordinary differential equations
A numerical integration method for ordinary differential equations is an algorithmic procedure that approximates the solution of an ODE over discrete steps by iteratively updating the dependent variable using information about its derivative.
-
C.
discrete analogue of differential calculus
A discrete analogue of differential calculus is a mathematical framework that extends concepts like derivatives, integrals, and differential equations to functions defined on discrete domains, typically using difference operators and summation.
-
D.
shock-capturing scheme
A shock-capturing scheme is a numerical method for solving hyperbolic partial differential equations that automatically resolves shock waves and discontinuities without explicitly tracking their locations, typically using conservative formulations and nonlinear limiters.
-
E.
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.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
Created at: April 10, 2026, 5:33 a.m.