Triple
T1807331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | variational autoencoders |
E40250
|
entity |
| Predicate | optimize |
P32130
|
FINISHED |
| Object | variational lower bound |
—
|
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: variational lower bound | Statement: [variational autoencoders, optimize, variational lower bound]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimize Context triple: [variational autoencoders, optimize, variational lower bound]
-
A.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
B.
performance
Indicates that one entity’s effectiveness, quality, or success in carrying out a task, function, or role is being evaluated or characterized in relation to some standard or expectation.
-
C.
powerOptimizationFor
Indicates a relationship where one entity is used to improve, manage, or optimize the power consumption or power efficiency of another entity.
-
D.
operation
Indicates that one entity performs, carries out, or controls the functioning of another entity or system.
-
E.
improvesOn
Indicates that one entity enhances, refines, or performs better than another entity, typically by addressing its limitations or increasing its effectiveness.
- 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_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab694d75ac8190a4d61399c04b9fb9 |
completed | March 6, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69aa61d6b8ec8190a1597b2e44ea6534 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab694bf6a08190a02ce2fc979e6701 |
completed | March 6, 2026, 11:54 p.m. |
Created at: March 4, 2026, 7:32 p.m.