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.