Triple

T4369863
Position Surface form Disambiguated ID Type / Status
Subject Julius Bernstein E98868 entity
Predicate influenced P9 FINISHED
Object Hodgkin–Huxley model of the action potential E381979 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: Hodgkin–Huxley model of the action potential | Statement: [Julius Bernstein, influenced, Hodgkin–Huxley model of the action potential]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hodgkin–Huxley model of the action potential
Context triple: [Julius Bernstein, influenced, Hodgkin–Huxley model of the action potential]
  • A. Hodgkin–Huxley model chosen
    The Hodgkin–Huxley model is a mathematical description of how action potentials in neurons are initiated and propagated through voltage-gated ion channels in the cell membrane.
  • B. all-or-none principle in nerve excitation
    The all-or-none principle in nerve excitation is the physiological rule that a nerve fiber, once stimulated beyond a certain threshold, responds with a full, uniform action potential rather than a graded response.
  • C. The Computational Brain
    The Computational Brain is an influential book that explores how principles of computation and neural networks can explain brain function and cognition.
  • 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. Untersuchungen über tierische Elektricität
    Untersuchungen über tierische Elektricität is a foundational 19th-century scientific work that systematically established the electrical nature of nerve and muscle activity.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b352052b388190b02cca9a3b480be4 completed March 12, 2026, 11:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5e50838188190b5b698d4bd2b5784 completed March 14, 2026, 10:45 p.m.
Created at: March 12, 2026, 11:17 p.m.