Triple

T27025494
Position Surface form Disambiguated ID Type / Status
Subject Godunov's method E680772 entity
Predicate instanceOf P0 FINISHED
Object method for hyperbolic partial differential equations C22665 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: method for hyperbolic partial differential equations
Context triple: [Godunov's method, instanceOf, method for hyperbolic partial differential equations]
  • A. method in differential equations
    A method in differential equations is a systematic procedure or algorithm used to find exact or approximate solutions to equations involving unknown functions and their derivatives.
  • B. 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.
  • C. shock-capturing scheme chosen
    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.
  • D. 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.
  • E. 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.
  • 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_69eeeb5450988190bfc9a3c012ac463a completed April 27, 2026, 4:51 a.m.
Created at: April 27, 2026, 7:10 a.m.