Triple
T36489160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | connectionism |
E899009
|
entity |
| Predicate | learningMechanism |
P94878
|
FINISHED |
| Object | weight adjustment based on experience |
—
|
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: weight adjustment based on experience | Statement: [connectionism, learningMechanism, weight adjustment based on experience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningMechanism Context triple: [connectionism, learningMechanism, weight adjustment based on experience]
-
A.
learningModel
Indicates that one entity functions as a learning model used to learn from data or examples in relation to another entity.
-
B.
usesLearningMechanism
chosen
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
C.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
D.
supportsLearningMechanism
Indicates that one entity facilitates, enables, or enhances the learning mechanism or process of another entity.
-
E.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
- 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_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:10 p.m.