Triple
T364220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boltzmann machines |
E7922
|
entity |
| Predicate | trainingObjective |
P12747
|
FINISHED |
| Object | maximize data log-likelihood |
—
|
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: maximize data log-likelihood | Statement: [Boltzmann machines, trainingObjective, maximize data log-likelihood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingObjective Context triple: [Boltzmann machines, trainingObjective, maximize data log-likelihood]
-
A.
training
Indicates that one entity is teaching, coaching, or otherwise helping another entity acquire or improve a skill, behavior, or capability.
-
B.
secondaryGoal
Indicates that something serves as a subordinate or supporting objective in addition to a primary goal.
-
C.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
D.
strategicGoal
Indicates that one entity represents a long-term objective or desired outcome that another entity is intentionally aiming to achieve or align actions toward.
-
E.
typicalTraining
Indicates that an entity commonly undergoes or is associated with a standard or usual form of training in relation to another entity or context.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebd1016481909b8ba3b047a47145 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95dbb208190b277fc5352a4ee84 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2eafc8da88190b4a05182f4384442 |
completed | Feb. 28, 2026, 1:17 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.