Triple

T11108853
Position Surface form Disambiguated ID Type / Status
Subject John Hopfield E262701 entity
Predicate notableWork P4 FINISHED
Object Neural networks and physical systems with emergent collective computational abilities E46142 NE 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: Neural networks and physical systems with emergent collective computational abilities | Statement: [John Hopfield, notableWork, Neural networks and physical systems with emergent collective computational abilities]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Neural networks and physical systems with emergent collective computational abilities
Context triple: [John Hopfield, notableWork, Neural networks and physical systems with emergent collective computational abilities]
  • A. Hopfield networks chosen
    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.
  • B. Intriguing properties of neural networks
    "Intriguing properties of neural networks" is a highly influential research paper that revealed surprising vulnerabilities and behaviors of deep neural networks, particularly their susceptibility to adversarial examples.
  • C. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • D. SyNAPSE neuromorphic computing program
    The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
  • E. Cascade-Correlation learning architecture
    Cascade-Correlation learning architecture is a neural network training method that incrementally builds its own topology by adding new hidden units during learning to improve performance.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d6aa9b46cc8190b19f9f0cc45bf322 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79a67d10c8190815d4c27d55270e8 completed April 9, 2026, 12:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69e42d72f8f48190a7414119a6be9d5e completed April 19, 2026, 1:18 a.m.
Created at: April 8, 2026, 9:27 p.m.