Triple
T6993306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Proximal Policy Optimization |
E162136
|
entity |
| Predicate | objectiveType |
P72446
|
FINISHED |
| Object | surrogate objective |
—
|
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: surrogate objective | Statement: [Proximal Policy Optimization, objectiveType, surrogate objective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: objectiveType Context triple: [Proximal Policy Optimization, objectiveType, surrogate objective]
-
A.
usesObjective
Indicates that an agent employs or applies a particular object, tool, or resource to carry out an action or achieve a goal.
-
B.
goalType
chosen
Indicates the specific category or nature of a goal associated with an entity or action.
-
C.
objectiveIncludes
Indicates that a broader objective encompasses or contains a specific sub-objective, component, or element as part of its scope.
-
D.
commercialObjective
Indicates that an action, plan, or entity is primarily intended to achieve commercial or business-related goals, such as generating revenue, profit, or market advantage.
-
E.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
- 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_69c68856d7808190ab33ee914640281b |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbc30fdc81909244d83c8178755c |
completed | March 27, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c4a18881908d267137daed828b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:32 p.m.