Triple

T9756364
Position Surface form Disambiguated ID Type / Status
Subject Boltzmann collision operator E236562 entity
Predicate approximatedBy P4447 FINISHED
Object Monte Carlo collision algorithms E86905 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: Monte Carlo collision algorithms | Statement: [Boltzmann collision operator, approximatedBy, Monte Carlo collision algorithms]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Monte Carlo collision algorithms
Context triple: [Boltzmann collision operator, approximatedBy, Monte Carlo collision algorithms]
  • A. Monte Carlo method chosen
    The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic systems.
  • B. Computer experiments on classical fluids
    "Computer experiments on classical fluids" is a pioneering work in computational physics that used numerical simulations to study the behavior and dynamics of classical fluid systems.
  • C. Dynamic Albedo of Neutrons
    Dynamic Albedo of Neutrons (DAN) is a neutron detector instrument on NASA’s Curiosity rover used to measure hydrogen and infer subsurface water content on Mars.
  • D. 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.
  • E. Particle & Particle Systems Characterization
    Particle & Particle Systems Characterization is a peer-reviewed scientific journal focusing on the characterization, properties, and behavior of particles and particulate systems across chemistry, materials science, and related fields.
  • 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9fb2889481908fba4a449d5007fe completed April 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1b02ff0f4819080410d4e7e809a24 completed April 5, 2026, 12:43 a.m.
Created at: March 30, 2026, 8:24 p.m.