Triple
T17521125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soft Actor-Critic |
E426679
|
entity |
| Predicate | trainingStability |
P127774
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Soft Actor-Critic, trainingStability, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingStability Context triple: [Soft Actor-Critic, trainingStability, high]
-
A.
trainingStatus
Indicates the current state or phase of an entity within a training or learning process.
-
B.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
C.
trainingUnder
Indicates that one entity is receiving instruction, guidance, or mentorship from another, typically in a subordinate or apprentice-like capacity.
-
D.
training
Indicates that one entity is teaching, coaching, or otherwise helping another entity acquire or improve a skill, behavior, or capability.
-
E.
trainingBalance
Indicates the remaining amount or status of training resources, sessions, or credits available in a given training relationship or program.
- F. None of above. chosen
Provenance (4 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f8b9888190aa8a45e09acf4319 |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb37d148190b7f38599c06594ee |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:49 a.m.