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.