Triple
T7874838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diederik P. Kingma |
E182823
|
entity |
| Predicate | VAETraining |
P18693
|
FINISHED |
| Object | backpropagation through stochastic nodes via reparameterization |
—
|
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: backpropagation through stochastic nodes via reparameterization | Statement: [Diederik P. Kingma, VAETraining, backpropagation through stochastic nodes via reparameterization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: VAETraining Context triple: [Diederik P. Kingma, VAETraining, backpropagation through stochastic nodes via reparameterization]
-
A.
trainingModel
chosen
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
B.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
C.
trainerVariant
Indicates a relationship where one trainer is an alternative or modified version of another trainer.
-
D.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
-
E.
typicalVineTraining
Indicates that one entity is the standard or commonly used method of training or shaping the growth of another entity, typically in a vine or climbing context.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39a961188190b2f12f8fe5d66641 |
completed | March 31, 2026, 3:04 a.m. |
| PD | Predicate disambiguation | batch_69cae928e1b88190b0620f4c4f03bc7d |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:56 p.m.