Triple
T1807337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | variational autoencoders |
E40250
|
entity |
| Predicate | learn |
P32131
|
FINISHED |
| Object | probabilistic latent representations |
—
|
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: probabilistic latent representations | Statement: [variational autoencoders, learn, probabilistic latent representations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learn Context triple: [variational autoencoders, learn, probabilistic latent representations]
-
A.
lesson
Indicates that one entity provides or conducts an instructional session or teaching activity for another entity.
-
B.
training
Indicates that one entity is teaching, coaching, or otherwise helping another entity acquire or improve a skill, behavior, or capability.
-
C.
trainingLeadsTo
Indicates that a process of training results in or brings about a particular outcome, state, or effect.
-
D.
earnOn
Indicates that one entity gains income, profit, or returns as a result of another entity or activity.
-
E.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
- 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.