Triple
T4470483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DDPG |
E98481
|
entity |
| Predicate | uses |
P98
|
FINISHED |
| Object | Ornstein-Uhlenbeck noise |
E48273
|
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: Ornstein-Uhlenbeck noise | Statement: [DDPG, uses, Ornstein-Uhlenbeck noise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ornstein-Uhlenbeck noise Context triple: [DDPG, uses, Ornstein-Uhlenbeck noise]
-
A.
Ornstein–Uhlenbeck process
chosen
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.
-
B.
DDPG
DDPG (Deep Deterministic Policy Gradient) is a model-free, off-policy deep reinforcement learning algorithm designed for continuous action spaces, combining ideas from DQN and actor-critic methods.
-
C.
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.
-
D.
Langevin Block
The Langevin Block is a historic federal government office building in Ottawa, Canada, that houses the Prime Minister’s Office and senior staff.
-
E.
Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356b6a1f48190a39f5411648c40ff |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6286c75b08190bd683d300f6c97f0 |
completed | March 15, 2026, 3:33 a.m. |
Created at: March 12, 2026, 11:34 p.m.