Triple

T11002318
Position Surface form Disambiguated ID Type / Status
Subject Hamiltonian Monte Carlo E260030 entity
Predicate typicallyUses P4791 FINISHED
Object leapfrog integrator
A leapfrog integrator is a symplectic numerical method for solving Hamiltonian dynamics that conserves energy well over long simulations, making it especially useful in physics and Hamiltonian Monte Carlo.
E898982 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: leapfrog integrator | Statement: [Hamiltonian Monte Carlo, typicallyUses, leapfrog integrator]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: leapfrog integrator
Context triple: [Hamiltonian Monte Carlo, typicallyUses, leapfrog integrator]
  • 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. Heun’s method
    Heun’s method is a second-order Runge–Kutta numerical integration technique that improves on Euler’s method by using a predictor-corrector approach to achieve greater accuracy.
  • C. Runge–Kutta methods
    Runge–Kutta methods are a family of iterative techniques for numerically solving ordinary differential equations with higher accuracy than simple one-step schemes.
  • D. 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.
  • E. 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.
  • 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: leapfrog integrator
Triple: [Hamiltonian Monte Carlo, typicallyUses, leapfrog integrator]
Generated description
A leapfrog integrator is a symplectic numerical method for solving Hamiltonian dynamics that conserves energy well over long simulations, making it especially useful in physics and Hamiltonian Monte Carlo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: leapfrog integrator
Target entity description: A leapfrog integrator is a symplectic numerical method for solving Hamiltonian dynamics that conserves energy well over long simulations, making it especially useful in physics and Hamiltonian Monte Carlo.
  • 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. Heun’s method
    Heun’s method is a second-order Runge–Kutta numerical integration technique that improves on Euler’s method by using a predictor-corrector approach to achieve greater accuracy.
  • C. Runge–Kutta methods
    Runge–Kutta methods are a family of iterative techniques for numerically solving ordinary differential equations with higher accuracy than simple one-step schemes.
  • D. 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.
  • E. 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.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d796d760008190930228fa77b61b8b completed April 9, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
NEDg Description generation batch_69e35570b0bc8190a939b0c8e3ce8105 completed April 18, 2026, 9:57 a.m.
NED2 Entity disambiguation (via description) batch_69e359508a388190a16d48a17015e13e completed April 18, 2026, 10:13 a.m.
Created at: April 8, 2026, 9:25 p.m.