Triple
T5910841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alex Krizhevsky |
E131452
|
entity |
| Predicate | paperPublishedIn |
P309
|
FINISHED |
| Object | Advances in Neural Information Processing Systems (NIPS 2012) |
E96742
|
NE FINISHED |
How this triple was built (3 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: Advances in Neural Information Processing Systems (NIPS 2012) | Statement: [Alex Krizhevsky, paperPublishedIn, Advances in Neural Information Processing Systems (NIPS 2012)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Advances in Neural Information Processing Systems (NIPS 2012) Context triple: [Alex Krizhevsky, paperPublishedIn, Advances in Neural Information Processing Systems (NIPS 2012)]
-
A.
NIPS
NIPS is the acronym for the Northern Ireland Prison Service, the government agency responsible for managing prisons and overseeing the custody and rehabilitation of offenders in Northern Ireland.
-
B.
“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.
-
C.
NeurIPS
chosen
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
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.
“Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: paperPublishedIn Context triple: [Alex Krizhevsky, paperPublishedIn, Advances in Neural Information Processing Systems (NIPS 2012)]
-
A.
publishedIn
chosen
Indicates that a work (such as an article, paper, or book) has been formally released or made available within a specific venue, medium, or publication.
-
B.
publishedDocument
Indicates that an entity has formally issued or made a document publicly available.
-
C.
publicationType
Indicates the specific category or format of a published work that characterizes how it is issued or presented.
-
D.
publicationBody
Indicates the organization or entity that serves as the publishing body responsible for issuing the referenced work.
-
E.
publicationAbout
Indicates that a publication has content whose subject or focus is the referenced entity.
- F. None of above.
Provenance (4 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b17375488190a3053d37712501b3 |
completed | March 23, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.