Triple

T2373449
Position Surface form Disambiguated ID Type / Status
Subject Markov chain Monte Carlo E46140 entity
Predicate hasMethod P859 FINISHED
Object Metropolis algorithm
The Metropolis algorithm is a foundational Markov chain Monte Carlo method used to sample from complex probability distributions by accepting or rejecting proposed moves according to a specific probabilistic rule.
 E260028 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: Metropolis algorithm | Statement: [Markov chain Monte Carlo, hasMethod, Metropolis algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Metropolis algorithm
Context triple: [Markov chain Monte Carlo, hasMethod, Metropolis algorithm]
  • A. Markov chain Monte Carlo
    Markov chain Monte Carlo is a class of algorithms that uses Markov chains to generate samples from complex probability distributions, widely used in Bayesian inference, statistical physics, and machine learning.
  • B. 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.
  • C. Monte Carlo
    Monte Carlo is a famous district of Monaco renowned for its luxury casinos, upscale resorts, and role as a glamorous hub for high-end tourism and events like the Monaco Grand Prix.
  • 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. 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.
  • 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: Metropolis algorithm
Triple: [Markov chain Monte Carlo, hasMethod, Metropolis algorithm]
Generated description
The Metropolis algorithm is a foundational Markov chain Monte Carlo method used to sample from complex probability distributions by accepting or rejecting proposed moves according to a specific probabilistic rule.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Metropolis algorithm
Target entity description: The Metropolis algorithm is a foundational Markov chain Monte Carlo method used to sample from complex probability distributions by accepting or rejecting proposed moves according to a specific probabilistic rule.
  • A. Markov chain Monte Carlo
    Markov chain Monte Carlo is a class of algorithms that uses Markov chains to generate samples from complex probability distributions, widely used in Bayesian inference, statistical physics, and machine learning.
  • B. 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.
  • C. Monte Carlo
    Monte Carlo is a famous district of Monaco renowned for its luxury casinos, upscale resorts, and role as a glamorous hub for high-end tourism and events like the Monaco Grand Prix.
  • 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. 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.
  • 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_69a88a145268819083e2736cb835c696 completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc791c4688190a4b8f0e540e84eb4 completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea8a619d48190b1e1ad4c3efaf130 completed March 9, 2026, 11:01 a.m.
NEDg Description generation batch_69aea93ddce88190a268bed11c5a7167 completed March 9, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_69aea9b8dff08190a09f0c965dfd6738 completed March 9, 2026, 11:06 a.m.
Created at: March 4, 2026, 7:56 p.m.