Triple

T26377184
Position Surface form Disambiguated ID Type / Status
Subject Bernard Widrow E660931 entity
Predicate knownFor P22 FINISHED
Object Widrow–Hoff learning rule
The Widrow–Hoff learning rule is a foundational adaptive algorithm for training linear neurons and perceptrons by iteratively minimizing the mean squared error between desired and actual outputs.
 E1721535 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: Widrow–Hoff learning rule | Statement: [Bernard Widrow, knownFor, Widrow–Hoff learning rule]
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: Widrow–Hoff learning rule
Triple: [Bernard Widrow, knownFor, Widrow–Hoff learning rule]
Generated description
The Widrow–Hoff learning rule is a foundational adaptive algorithm for training linear neurons and perceptrons by iteratively minimizing the mean squared error between desired and actual outputs.

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_69ee812a698881908d6a58265995fa39 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f61070d7348190ac0ac38a0249d2b8 completed May 2, 2026, 2:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a119a7766748190be5bbce911cc2886 completed May 23, 2026, 12:15 p.m.
NEDg Description generation batch_6a119b150b7c81909265302179aef83e completed May 23, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a119c7d5eac8190ae6fbb97bf64b472 completed May 23, 2026, 12:24 p.m.
Created at: April 26, 2026, 11:02 p.m.