Triple

T3717864
Position Surface form Disambiguated ID Type / Status
Subject Alan Hodgkin E81573 entity
Predicate knownFor P22 FINISHED
Object Hodgkin–Huxley model
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.
E381979 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: Hodgkin–Huxley model | Statement: [Alan Hodgkin, knownFor, Hodgkin–Huxley model]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hodgkin–Huxley model
Context triple: [Alan Hodgkin, knownFor, Hodgkin–Huxley model]
  • A. 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.
  • B. SNN
    SNN is the National Rail station code assigned to Swinton railway station in South Yorkshire, England.
  • C. Ian Hodgkin
    Ian Hodgkin is a person notable enough to be recognized as a bearer of the Hodgkin surname.
  • D. Hebbian learning
    Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
  • E. 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.
  • 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: Hodgkin–Huxley model
Triple: [Alan Hodgkin, knownFor, Hodgkin–Huxley model]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Hodgkin–Huxley model
Target entity description: 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.
  • A. 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.
  • B. SNN
    SNN is the National Rail station code assigned to Swinton railway station in South Yorkshire, England.
  • C. Ian Hodgkin
    Ian Hodgkin is a person notable enough to be recognized as a bearer of the Hodgkin surname.
  • D. Hebbian learning
    Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
  • E. 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.
  • 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_69ad8b1a81588190b3f27a5483bb610e completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adca984844819087a2f6b20d2f19e7 completed March 8, 2026, 7:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4ce1260948190b4707337e9427c2c completed March 14, 2026, 2:55 a.m.
NEDg Description generation batch_69b4cf799ae88190bbf821f4c4500031 completed March 14, 2026, 3:01 a.m.
NED2 Entity disambiguation (via description) batch_69b4d0057fe8819092a40732324f88c9 completed March 14, 2026, 3:03 a.m.
Created at: March 8, 2026, 3:33 p.m.