Triple

T2815532
Position Surface form Disambiguated ID Type / Status
Subject Euler’s method for numerical integration E54272 entity
Predicate isGeneralizedBy P2372 FINISHED
Object classical fourth-order Runge–Kutta method
The classical fourth-order Runge–Kutta method is a widely used, higher-accuracy numerical technique for solving ordinary differential equations by combining multiple intermediate slope evaluations within each integration step.
E300768 NE FINISHED

How this triple was built (4 steps)

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.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: classical fourth-order Runge–Kutta method | Statement: [Euler’s method for numerical integration, isGeneralizedBy, classical fourth-order Runge–Kutta method]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: classical fourth-order Runge–Kutta method
Context triple: [Euler’s method for numerical integration, isGeneralizedBy, classical fourth-order Runge–Kutta method]
  • A. Euler’s method for numerical integration
    Euler’s method for numerical integration is a simple first-order numerical procedure used to approximate solutions to ordinary differential equations by stepping forward in small increments.
  • B. Milstein method
    The Milstein method is a numerical scheme for solving stochastic differential equations that improves on the Euler–Maruyama method by including derivative terms of the diffusion coefficient for higher accuracy.
  • C. Euler–Maruyama method
    The Euler–Maruyama method is a basic time-stepping scheme for numerically approximating solutions to stochastic differential equations, widely used in simulations of systems with noise such as Langevin dynamics.
  • D. Crank–Nicolson scheme
    The Crank–Nicolson scheme is a finite difference method for numerically solving time-dependent partial differential equations, especially parabolic ones like the heat equation, known for its second-order accuracy and unconditional stability.
  • E. Feautrier method
    The Feautrier method is a numerical technique used in radiative transfer to stably and accurately solve second-order differential equations for the radiation field in stellar atmospheres and similar media.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: classical fourth-order Runge–Kutta method
Triple: [Euler’s method for numerical integration, isGeneralizedBy, classical fourth-order Runge–Kutta method]
Generated description
The classical fourth-order Runge–Kutta method is a widely used, higher-accuracy numerical technique for solving ordinary differential equations by combining multiple intermediate slope evaluations within each integration step.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: classical fourth-order Runge–Kutta method
Target entity description: The classical fourth-order Runge–Kutta method is a widely used, higher-accuracy numerical technique for solving ordinary differential equations by combining multiple intermediate slope evaluations within each integration step.
  • A. Euler’s method for numerical integration
    Euler’s method for numerical integration is a simple first-order numerical procedure used to approximate solutions to ordinary differential equations by stepping forward in small increments.
  • B. Milstein method
    The Milstein method is a numerical scheme for solving stochastic differential equations that improves on the Euler–Maruyama method by including derivative terms of the diffusion coefficient for higher accuracy.
  • C. Euler–Maruyama method
    The Euler–Maruyama method is a basic time-stepping scheme for numerically approximating solutions to stochastic differential equations, widely used in simulations of systems with noise such as Langevin dynamics.
  • D. Crank–Nicolson scheme
    The Crank–Nicolson scheme is a finite difference method for numerically solving time-dependent partial differential equations, especially parabolic ones like the heat equation, known for its second-order accuracy and unconditional stability.
  • E. Feautrier method
    The Feautrier method is a numerical technique used in radiative transfer to stably and accurately solve second-order differential equations for the radiation field in stellar atmospheres and similar media.
  • F. None of above. chosen

Provenance (5 batches)

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_69ab49de0af08190b3da69683be1e728 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde4d29488190a32461906dd9ea7e completed March 7, 2026, 8:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69afce9f964081909e422aaf1f026dbb completed March 10, 2026, 7:56 a.m.
NEDg Description generation batch_69afcf12e3a0819098f28d31434a0c5f completed March 10, 2026, 7:58 a.m.
NED2 Entity disambiguation (via description) batch_69afcf9c2d308190b111aa8038c9227a completed March 10, 2026, 8 a.m.
Created at: March 6, 2026, 9:59 p.m.