Triple
T13267031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Radford M. Neal |
E315948
|
entity |
| Predicate | mainInterest |
P1074
|
FINISHED |
| Object | Monte Carlo methods |
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 methods | Statement: [Radford M. Neal, mainInterest, Monte Carlo methods]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo methods Context triple: [Radford M. Neal, mainInterest, Monte Carlo methods]
-
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.
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.
-
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.
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.
-
E.
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.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9901e44bc8190966f87ae219d6bf4 |
completed | April 11, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a4cc20881909b1ca6623e5b1988 |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:25 p.m.