Triple
T4470179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dueling DQN |
E98474
|
entity |
| Predicate | normalizesAdvantageStream |
P19552
|
FINISHED |
| Object | by subtracting mean advantage |
—
|
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: by subtracting mean advantage | Statement: [Dueling DQN, normalizesAdvantageStream, by subtracting mean advantage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: normalizesAdvantageStream Context triple: [Dueling DQN, normalizesAdvantageStream, by subtracting mean advantage]
-
A.
normalizationAttempt
chosen
Indicates an effort to convert something into a standard or consistent form according to defined rules or criteria.
-
B.
equalizingGoalBy
Indicates that one entity has the objective of reducing differences or disparities between itself and another entity.
-
C.
normIs
Indicates that something conforms to, or is characterized by, a particular standard, rule, or norm.
-
D.
notableAdvantage
Indicates that one entity possesses a significant benefit, edge, or favorable quality over another entity or in a given context.
-
E.
normativeGoal
Indicates that one entity is a desired or prescribed objective, standard, or end state that another entity ought to pursue or realize.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356fb69a0819099f0005779f4fcac |
completed | March 13, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69b3563bf4f8819081726cde3a34460b |
completed | March 13, 2026, 12:11 a.m. |
Created at: March 12, 2026, 11:34 p.m.