Triple
T4861959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleanor J. Gibson |
E108680
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Gibsonian theory of perceptual learning
The Gibsonian theory of perceptual learning is a psychological framework proposing that perception improves through direct interaction with the environment, as individuals learn to detect increasingly subtle and useful information (or "invariants") in sensory input without relying on internal representations.
|
E474500
|
NE FINISHED |
How this triple was built (4 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: Gibsonian theory of perceptual learning | Statement: [Eleanor J. Gibson, knownFor, Gibsonian theory of perceptual learning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gibsonian theory of perceptual learning Context triple: [Eleanor J. Gibson, knownFor, Gibsonian theory of perceptual learning]
-
A.
Unified Theories of Cognition
Unified Theories of Cognition is a comprehensive cognitive science framework proposed by Allen Newell that seeks to explain diverse mental processes—such as problem solving, memory, and learning—within a single, unified theoretical architecture.
-
B.
The Psychology of Computer Vision (edited volume)
The Psychology of Computer Vision is an influential edited volume, compiled by Patrick Henry Winston, that brings together foundational research exploring how principles of human perception and cognition can inform and advance computer vision.
-
C.
Learning to See by Moving
"Learning to See by Moving" is a research work in computer vision that explores how visual understanding can emerge from an agent’s own movement and interaction with the environment, rather than from static images alone.
-
D.
On the Theory of Objective Mind
"On the Theory of Objective Mind" is a philosophical work that explores how shared, externalized structures of thought and culture constitute an objective dimension of mind beyond individual subjectivity.
-
E.
Principles of Topological Psychology
Principles of Topological Psychology is a foundational 1936 work by Kurt Lewin that introduces a mathematical, field-theoretic approach to understanding psychological behavior and experience in terms of topological spaces.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gibsonian theory of perceptual learning Triple: [Eleanor J. Gibson, knownFor, Gibsonian theory of perceptual learning]
Generated description
The Gibsonian theory of perceptual learning is a psychological framework proposing that perception improves through direct interaction with the environment, as individuals learn to detect increasingly subtle and useful information (or "invariants") in sensory input without relying on internal representations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gibsonian theory of perceptual learning Target entity description: The Gibsonian theory of perceptual learning is a psychological framework proposing that perception improves through direct interaction with the environment, as individuals learn to detect increasingly subtle and useful information (or "invariants") in sensory input without relying on internal representations.
-
A.
Unified Theories of Cognition
Unified Theories of Cognition is a comprehensive cognitive science framework proposed by Allen Newell that seeks to explain diverse mental processes—such as problem solving, memory, and learning—within a single, unified theoretical architecture.
-
B.
The Psychology of Computer Vision (edited volume)
The Psychology of Computer Vision is an influential edited volume, compiled by Patrick Henry Winston, that brings together foundational research exploring how principles of human perception and cognition can inform and advance computer vision.
-
C.
Learning to See by Moving
"Learning to See by Moving" is a research work in computer vision that explores how visual understanding can emerge from an agent’s own movement and interaction with the environment, rather than from static images alone.
-
D.
On the Theory of Objective Mind
"On the Theory of Objective Mind" is a philosophical work that explores how shared, externalized structures of thought and culture constitute an objective dimension of mind beyond individual subjectivity.
-
E.
Principles of Topological Psychology
Principles of Topological Psychology is a foundational 1936 work by Kurt Lewin that introduces a mathematical, field-theoretic approach to understanding psychological behavior and experience in terms of topological spaces.
- F. None of above. chosen
Provenance (5 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_69bd440b965081908b0557721cae6338 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d5f62b48190b367ed1b850cfbcb |
completed | March 20, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5cf921cc8190a092bb69c1981890 |
completed | March 21, 2026, 8:55 a.m. |
| NEDg | Description generation | batch_69be5e833ca88190b89dda5cafb180fe |
completed | March 21, 2026, 9:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be5eed97a08190afd99a0700f1f4c7 |
completed | March 21, 2026, 9:03 a.m. |
Created at: March 20, 2026, 1:26 p.m.