Triple
T11002372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helmholtz machine |
E260031
|
entity |
| Predicate | comparedTo |
P278
|
FINISHED |
| Object | Boltzmann machine |
E7922
|
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: Boltzmann machine | Statement: [Helmholtz machine, comparedTo, Boltzmann machine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boltzmann machine Context triple: [Helmholtz machine, comparedTo, Boltzmann machine]
-
A.
Boltzmann machines
chosen
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
-
B.
Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
-
C.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
D.
Deep belief networks
Deep belief networks are a class of deep generative neural network models composed of stacked layers of latent variables, typically built from restricted Boltzmann machines, used for unsupervised feature learning and representation.
-
E.
“A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d796d760008190930228fa77b61b8b |
completed | April 9, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3453d181081908cb58a957f4d1295 |
completed | April 18, 2026, 8:47 a.m. |
Created at: April 8, 2026, 9:25 p.m.