Triple
T19755992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gibsonian theory of perceptual learning |
E474500
|
entity |
| Predicate | viewsLearningAs |
P4078
|
FINISHED |
| Object | increasing attunement to ecological information |
—
|
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: increasing attunement to ecological information | Statement: [Gibsonian theory of perceptual learning, viewsLearningAs, increasing attunement to ecological information]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: viewsLearningAs Context triple: [Gibsonian theory of perceptual learning, viewsLearningAs, increasing attunement to ecological information]
-
A.
viewsLearnerAs
Indicates that one entity perceives, regards, or treats another entity specifically in the role of a learner.
-
B.
viewOnEducation
chosen
Indicates a stance, opinion, or perspective that an entity holds regarding education or educational matters.
-
C.
viewOnKnowledge
Indicates the perspective, stance, or opinion one entity holds regarding another entity’s knowledge or understanding.
-
D.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
E.
structureLearning
Indicates a process in which an agent infers or constructs the underlying structure or dependency relationships within a set of variables, data, or a model.
- 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6531afcbc8190bd5364700008f6d8 |
completed | April 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69e5305016e08190b9561a96baecb0b8 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.