Triple
T7874769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adam: A Method for Stochastic Optimization |
E182822
|
entity |
| Predicate | comparesWith |
P278
|
FINISHED |
| Object | RMSProp |
E134579
|
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: RMSProp | Statement: [Adam: A Method for Stochastic Optimization, comparesWith, RMSProp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RMSProp Context triple: [Adam: A Method for Stochastic Optimization, comparesWith, RMSProp]
-
A.
RMSProp
chosen
RMSProp is an adaptive gradient-based optimization algorithm commonly used to efficiently train deep neural networks by adjusting learning rates for individual parameters.
-
B.
AdaGrad
AdaGrad is an adaptive gradient descent optimization algorithm that adjusts learning rates for individual parameters based on their historical gradients, often improving convergence in sparse settings.
-
C.
Adam optimizer
The Adam optimizer is a popular stochastic gradient descent method in machine learning that adaptively adjusts learning rates for each parameter using estimates of first and second moments of gradients.
-
D.
AdaDelta
AdaDelta is an adaptive learning rate optimization algorithm for training neural networks that improves upon methods like RMSProp by eliminating the need to manually set a global learning rate.
-
E.
“Stochastic Gradient Descent Tricks”
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39a961188190b2f12f8fe5d66641 |
completed | March 31, 2026, 3:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc561d31888190bae66fb009ace1a1 |
completed | March 31, 2026, 11:17 p.m. |
Created at: March 30, 2026, 4:56 p.m.