Triple
T18300531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Tune |
E438346
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | HyperBand |
—
|
NE NERFINISHED |
How this triple was built (3 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: HyperBand | Statement: [Ray Tune, supports, HyperBand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HyperBand Context triple: [Ray Tune, supports, HyperBand]
-
A.
Bayesian optimization
Bayesian optimization is a sample-efficient global optimization strategy that uses probabilistic surrogate models, typically Gaussian processes, to optimize expensive black-box functions with as few evaluations as possible.
-
B.
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms is a seminal research paper that introduced efficient Bayesian optimization techniques for automatically tuning hyperparameters of complex machine learning models.
-
C.
Optimates
The Optimates were a conservative political faction in the late Roman Republic that championed senatorial authority and traditional aristocratic privileges against popular reformers like Julius Caesar.
-
D.
Trust Region Policy Optimization
Trust Region Policy Optimization is a reinforcement learning algorithm that improves policy performance by making stable, constrained updates that limit how much each new policy can deviate from the previous one.
-
E.
Robbins–Monro algorithm
The Robbins–Monro algorithm is a foundational stochastic approximation method used to find the roots of functions when observations are corrupted by noise, forming the basis for many modern optimization and learning techniques.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HyperBand Target entity description: HyperBand is a hyperparameter optimization algorithm that efficiently allocates computational resources among many configurations using adaptive early-stopping and bandit-based strategies.
-
A.
Bayesian optimization
Bayesian optimization is a sample-efficient global optimization strategy that uses probabilistic surrogate models, typically Gaussian processes, to optimize expensive black-box functions with as few evaluations as possible.
-
B.
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms is a seminal research paper that introduced efficient Bayesian optimization techniques for automatically tuning hyperparameters of complex machine learning models.
-
C.
Optimates
The Optimates were a conservative political faction in the late Roman Republic that championed senatorial authority and traditional aristocratic privileges against popular reformers like Julius Caesar.
-
D.
Trust Region Policy Optimization
Trust Region Policy Optimization is a reinforcement learning algorithm that improves policy performance by making stable, constrained updates that limit how much each new policy can deviate from the previous one.
-
E.
Robbins–Monro algorithm
The Robbins–Monro algorithm is a foundational stochastic approximation method used to find the roots of functions when observations are corrupted by noise, forming the basis for many modern optimization and learning techniques.
- F. None of above. chosen
Provenance (2 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5017e88cc8190a969eb628ca1b496 |
completed | April 19, 2026, 4:23 p.m. |
Created at: April 10, 2026, 10:35 a.m.