Triple
T2225093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master of Financial Engineering |
E48630
|
entity |
| Predicate | focusesOn |
P31
|
FINISHED |
| Object | Monte Carlo simulation |
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 simulation | Statement: [Master of Financial Engineering, focusesOn, Monte Carlo simulation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo simulation Context triple: [Master of Financial Engineering, focusesOn, Monte Carlo simulation]
-
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.
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.
-
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.
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.
Sequential Analysis
Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
- 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_69a88aa51b388190949868ec9766e587 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc03ffcbc8190a27e32af831c7be5 |
completed | March 7, 2026, 6:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6562c9448190b53c068c900bf0bf |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:47 p.m.