Triple
T7874837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diederik P. Kingma |
E182823
|
entity |
| Predicate | VAEObjective |
P12747
|
FINISHED |
| Object | evidence 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: evidence lower bound | Statement: [Diederik P. Kingma, VAEObjective, evidence lower bound]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: VAEObjective Context triple: [Diederik P. Kingma, VAEObjective, evidence lower bound]
-
A.
trainingObjective
chosen
Indicates the goal or target outcome that a training process is designed to achieve.
-
B.
EVAobjective
Indicates that an extravehicular activity (EVA) is performed with the purpose of achieving a specific objective or goal.
-
C.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
D.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
E.
trainerVariant
Indicates a relationship where one trainer is an alternative or modified version of another trainer.
- 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.