Triple

T11002310
Position Surface form Disambiguated ID Type / Status
Subject Hamiltonian Monte Carlo E260030 entity
Predicate advantageOver P635 FINISHED
Object random-walk Metropolis E260028 NE FINISHED

How this triple was built (2 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: random-walk Metropolis | Statement: [Hamiltonian Monte Carlo, advantageOver, random-walk Metropolis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: random-walk Metropolis
Context triple: [Hamiltonian Monte Carlo, advantageOver, random-walk Metropolis]
  • A. Metropolis algorithm chosen
    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.
  • 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. 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.
  • D. Gibbs sampling
    Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
  • E. Kac walk
    The Kac walk is a probabilistic model introduced by mathematician Mark Kac to study the approach to equilibrium in kinetic theory via a simplified random process.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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.
Created at: April 8, 2026, 9:25 p.m.