Triple
T10023463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian networks |
E200666
|
entity |
| Predicate | parameterLearning |
P91763
|
FINISHED |
| Object | maximum likelihood estimation |
—
|
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: maximum likelihood estimation | Statement: [Bayesian networks, parameterLearning, maximum likelihood estimation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: parameterLearning Context triple: [Bayesian networks, parameterLearning, maximum likelihood estimation]
-
A.
parameter
Indicates that one entity serves as a parameter or argument that configures, constrains, or influences the behavior or outcome of another entity or process.
-
B.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
C.
typicalDefaultLearningRate
Indicates the standard or commonly used learning rate value typically applied by default in a learning or optimization process.
-
D.
predictionAlgorithm
Indicates a relationship where an algorithm generates predictions or forecasts about outcomes based on input data or observed patterns.
-
E.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
- F. None of above. chosen
Provenance (4 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd7c75548190aa604d90d63dc111 |
completed | April 2, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69cd4b7cd4208190b2253583ee2f892c |
completed | April 1, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:53 p.m.