Triple
T8482993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rainbow DQN |
E200562
|
entity |
| Predicate | optimizationAlgorithm |
P27179
|
FINISHED |
| Object | stochastic gradient descent variant |
—
|
LITERAL 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: stochastic gradient descent variant | Statement: [Rainbow DQN, optimizationAlgorithm, stochastic gradient descent variant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationAlgorithm Context triple: [Rainbow DQN, optimizationAlgorithm, stochastic gradient descent variant]
-
A.
supportsOptimizationAlgorithm
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
B.
optimizationSolver
Indicates a relationship where a solver entity is used to compute an optimal solution for a given optimization problem or task.
-
C.
optimizationType
chosen
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
D.
optimizationTarget
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
E.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
- F. None of above.
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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53845e881909eeb32863c7aa942 |
completed | March 31, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69cbd107633c8190a36ba50e07876918 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:12 p.m.