Triple
T1807340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | variational autoencoders |
E40250
|
entity |
| Predicate | assume |
P7027
|
FINISHED |
| Object | prior distribution over latent variables |
—
|
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: prior distribution over latent variables | Statement: [variational autoencoders, assume, prior distribution over latent variables]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: assume Context triple: [variational autoencoders, assume, prior distribution over latent variables]
-
A.
assumes
Indicates that one entity takes on, accepts, or presumes a role, responsibility, state, or fact regarding another entity or situation.
-
B.
allows
Indicates that one entity grants permission, capability, or opportunity for another entity to perform an action or be in a certain state.
-
C.
affirms
Indicates that one entity explicitly confirms, supports, or asserts the truth or validity of another entity, statement, or proposition.
-
D.
after
Indicates that one event, state, or action occurs later in time than another, following it in temporal order.
-
E.
typicalAssumption
chosen
Indicates that something is taken as a standard or default assumption that generally holds in typical or normal circumstances.
- 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_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. |
Created at: March 4, 2026, 7:32 p.m.