Triple
T5817757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David E. Rumelhart |
E129028
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Rumelhart |
E129028
|
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: Rumelhart | Statement: [David E. Rumelhart, familyName, Rumelhart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rumelhart Context triple: [David E. Rumelhart, familyName, Rumelhart]
-
A.
David E. Rumelhart
chosen
David E. Rumelhart was a pioneering cognitive psychologist and neural network researcher whose work on parallel distributed processing and backpropagation profoundly shaped modern cognitive science and machine learning.
-
B.
“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.
-
C.
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.
-
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.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
- 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_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_69c098504c68819089eff37ea1fa0979 |
completed | March 23, 2026, 1:33 a.m. |
Created at: March 22, 2026, 3:53 p.m.