Triple
T5817789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David E. Rumelhart |
E129028
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
An interactive activation model of context effects in letter perception
"An interactive activation model of context effects in letter perception" is a seminal cognitive psychology paper that introduced a computational model explaining how letter and word recognition are influenced by both bottom-up sensory input and top-down contextual information.
|
E548092
|
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: An interactive activation model of context effects in letter perception | Statement: [David E. Rumelhart, notableWork, An interactive activation model of context effects in letter perception]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: An interactive activation model of context effects in letter perception Context triple: [David E. Rumelhart, notableWork, An interactive activation model of context effects in letter perception]
-
A.
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.
-
B.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
C.
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.
-
D.
Hopfield networks
Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
-
E.
“A Semantic Model for Memory”
“A Semantic Model for Memory” is a foundational work in cognitive science and artificial intelligence that proposes how human memory can be represented and processed using structured semantic relationships.
- 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: An interactive activation model of context effects in letter perception Triple: [David E. Rumelhart, notableWork, An interactive activation model of context effects in letter perception]
Generated description
"An interactive activation model of context effects in letter perception" is a seminal cognitive psychology paper that introduced a computational model explaining how letter and word recognition are influenced by both bottom-up sensory input and top-down contextual information.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: An interactive activation model of context effects in letter perception Target entity description: "An interactive activation model of context effects in letter perception" is a seminal cognitive psychology paper that introduced a computational model explaining how letter and word recognition are influenced by both bottom-up sensory input and top-down contextual information.
-
A.
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.
-
B.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
C.
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.
-
D.
Hopfield networks
Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
-
E.
“A Semantic Model for Memory”
“A Semantic Model for Memory” is a foundational work in cognitive science and artificial intelligence that proposes how human memory can be represented and processed using structured semantic relationships.
- 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_69c0084869e881908d7859492183ca7b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c033e36cbc81908f1ef1a1a310674c |
completed | March 22, 2026, 6:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0985399488190bcab9702e3b88539 |
completed | March 23, 2026, 1:33 a.m. |
| NEDg | Description generation | batch_69c0990d00e88190b9f2b34a8cedda3a |
completed | March 23, 2026, 1:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c099770ca88190a91815ec055f6df8 |
completed | March 23, 2026, 1:37 a.m. |
Created at: March 22, 2026, 3:53 p.m.