Triple

T718434
Position Surface form Disambiguated ID Type / Status
Subject Stanislaw Ulam E14360 entity
Predicate coInvented P1858 FINISHED
Object 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.
E86905 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: Monte Carlo method | Statement: [Stanislaw Ulam, coInvented, Monte Carlo method]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monte Carlo method
Context triple: [Stanislaw Ulam, coInvented, Monte Carlo method]
  • 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. 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.
  • C. 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.
  • D. Langevin dynamics
    Langevin dynamics is a stochastic approach to modeling the motion of particles in a fluid by combining deterministic forces with random thermal fluctuations, often used to simulate Brownian motion and other nonequilibrium processes.
  • E. Ornstein–Uhlenbeck process
    The Ornstein–Uhlenbeck process is a continuous-time stochastic process that models mean-reverting random motion, widely used in physics and quantitative finance to describe systems fluctuating around a long-term equilibrium.
  • 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: Monte Carlo method
Triple: [Stanislaw Ulam, coInvented, Monte Carlo method]
Generated description
The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Monte Carlo method
Target entity description: The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic systems.
  • 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. 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.
  • C. 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.
  • D. Langevin dynamics
    Langevin dynamics is a stochastic approach to modeling the motion of particles in a fluid by combining deterministic forces with random thermal fluctuations, often used to simulate Brownian motion and other nonequilibrium processes.
  • E. Ornstein–Uhlenbeck process
    The Ornstein–Uhlenbeck process is a continuous-time stochastic process that models mean-reverting random motion, widely used in physics and quantitative finance to describe systems fluctuating around a long-term equilibrium.
  • 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_69a4934a36e081909e7abef98b898a4e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a58d4c3c8190ad4527d14bca5e6e completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a63759d6108190adcdeac45e4c7766 completed March 3, 2026, 1:20 a.m.
NEDg Description generation batch_69a63b158dc881909da0b90f498e9a43 completed March 3, 2026, 1:36 a.m.
NED2 Entity disambiguation (via description) batch_69a63eccc5a08190b39b7818dc61591c completed March 3, 2026, 1:52 a.m.
Created at: March 1, 2026, 7:37 p.m.