Triple
T7874834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diederik P. Kingma |
E182823
|
entity |
| Predicate | VAEComponent |
P2006
|
FINISHED |
| Object | latent variable modeling |
—
|
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: latent variable modeling | Statement: [Diederik P. Kingma, VAEComponent, latent variable modeling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: VAEComponent Context triple: [Diederik P. Kingma, VAEComponent, latent variable modeling]
-
A.
visualCompanion
Indicates that one entity serves as a visual counterpart, partner, or accompanying element to another in a visual context.
-
B.
model
chosen
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
C.
trainerVariant
Indicates a relationship where one trainer is an alternative or modified version of another trainer.
-
D.
varnaSystem
Indicates a relationship where individuals or groups are classified according to the traditional hierarchical varna (caste) 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_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.