Triple
T36487921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latent Dirichlet Allocation |
E898981
|
entity |
| Predicate | hyperparameterAlphaControls |
P123498
|
FINISHED |
| Object | document-topic sparsity |
—
|
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: document-topic sparsity | Statement: [Latent Dirichlet Allocation, hyperparameterAlphaControls, document-topic sparsity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hyperparameterAlphaControls Context triple: [Latent Dirichlet Allocation, hyperparameterAlphaControls, document-topic sparsity]
-
A.
regularizationControlledBy
Indicates that the regularization applied in a process, model, or system is governed, adjusted, or determined by a specific controlling factor or mechanism.
-
B.
controlParameter
chosen
Indicates that one entity functions as a parameter that governs, tunes, or constrains the behavior or operation of another entity.
-
C.
usesLearningRateParameter
Indicates that an entity employs a specific learning rate parameter when performing a learning or optimization process.
-
D.
typicalDefaultLearningRate
Indicates the standard or commonly used learning rate value typically applied by default in a learning or optimization process.
-
E.
parameterLearning
Indicates a process or relationship in which parameters of a model, system, or function are adjusted or inferred—typically from data—to improve performance or fit.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bccf05bc8190b61fdb2b2a315811 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:10 p.m.