Triple
T10023673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auto-Encoding Variational Bayes |
E200670
|
entity |
| Predicate | trainingCriterion |
P12747
|
FINISHED |
| Object | maximization of ELBO |
—
|
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: maximization of ELBO | Statement: [Auto-Encoding Variational Bayes, trainingCriterion, maximization of ELBO]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingCriterion Context triple: [Auto-Encoding Variational Bayes, trainingCriterion, maximization of ELBO]
-
A.
trainingObjective
chosen
Indicates the goal or target outcome that a training process is designed to achieve.
-
B.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
C.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
-
D.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
E.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd7c75548190aa604d90d63dc111 |
completed | April 2, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69cd4b7cd4208190b2253583ee2f892c |
completed | April 1, 2026, 4:44 p.m. |
Created at: March 30, 2026, 8:53 p.m.