Triple
T11003023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markov random field |
E260046
|
entity |
| Predicate | learningMethodsInclude |
P94878
|
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: [Markov random field, learningMethodsInclude, maximum likelihood estimation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningMethodsInclude Context triple: [Markov random field, learningMethodsInclude, maximum likelihood estimation]
-
A.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
B.
usesLearningMechanism
chosen
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
C.
machineLearningLibrary
Indicates that one entity is a software library or framework specifically designed to support machine learning tasks for another entity.
-
D.
usesNeuralNetworks
Indicates that one entity employs neural network models or techniques as part of its functioning, processing, or decision-making.
-
E.
supportsLearningMechanism
Indicates that one entity facilitates, enables, or enhances the learning mechanism or process of another entity.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797546f448190946ee6442d657dc5 |
completed | April 9, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d72e96be6c8190a46c69f61b2d8cd4 |
completed | April 9, 2026, 4:44 a.m. |
Created at: April 8, 2026, 9:25 p.m.