Triple
T1807362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | variational autoencoders |
E40250
|
entity |
| Predicate | haveVariant |
P455
|
FINISHED |
| Object | conditional variational autoencoders |
—
|
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: conditional variational autoencoders | Statement: [variational autoencoders, haveVariant, conditional variational autoencoders]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveVariant Context triple: [variational autoencoders, haveVariant, conditional variational autoencoders]
-
A.
hasVariant
chosen
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
B.
hasVariantSystem
Indicates that one system is an alternative or variant form of another system within the same general framework or category.
-
C.
hasVariantSeries
Indicates a relationship where one entity is a variant or alternative series derived from or associated with another series.
-
D.
hasVariance
Indicates that there is a measurable degree of variability or dispersion in the values or outcomes associated with the related entities.
-
E.
hasVariability
Indicates that an entity exhibits variation or fluctuation in its state, value, or characteristics over time or across instances.
- 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.