Triple

T1462643
Position Surface form Disambiguated ID Type / Status
Subject Euler–Maruyama method E31546 entity
Predicate comparedTo P278 FINISHED
Object 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.
E166677 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: Milstein method | Statement: [Euler–Maruyama method, comparedTo, Milstein method]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Milstein method
Context triple: [Euler–Maruyama method, comparedTo, Milstein method]
  • A. 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.
  • B. 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.
  • C. Darwin–Fowler method
    The Darwin–Fowler method is a statistical mechanics technique that uses complex analysis and generating functions to derive distribution laws for systems of many particles.
  • 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. Monte Carlo method
    The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic systems.
  • 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: Milstein method
Triple: [Euler–Maruyama method, comparedTo, Milstein method]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Milstein method
Target entity description: 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.
  • A. 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.
  • B. 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.
  • C. Darwin–Fowler method
    The Darwin–Fowler method is a statistical mechanics technique that uses complex analysis and generating functions to derive distribution laws for systems of many particles.
  • 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. Monte Carlo method
    The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic systems.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c5b6e36c81909c47b2f7e66f17d7 completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e7ab538819090bc3e3ed1bbff64 completed March 8, 2026, 5:51 a.m.
NEDg Description generation batch_69ad0f5106fc8190ab03c4e5a0287424 completed March 8, 2026, 5:55 a.m.
NED2 Entity disambiguation (via description) batch_69ad0fa4c7d48190ac84267c16c6eb00 completed March 8, 2026, 5:56 a.m.
Created at: March 1, 2026, 8 p.m.