Triple
T2169497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep belief networks |
E46988
|
entity |
| Predicate | publishedIn |
P309
|
FINISHED |
| Object | Neural Computation |
E80658
|
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: Neural Computation | Statement: [Deep belief networks, publishedIn, Neural Computation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neural Computation Context triple: [Deep belief networks, publishedIn, Neural Computation]
-
A.
Neural Computation
chosen
Neural Computation is a peer-reviewed scientific journal focusing on theoretical and computational aspects of neural systems, machine learning, and artificial intelligence.
-
B.
Intriguing properties of neural networks
"Intriguing properties of neural networks" is a highly influential research paper that revealed surprising vulnerabilities and behaviors of deep neural networks, particularly their susceptibility to adversarial examples.
-
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.
NeurIPS
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
E.
SyNAPSE neuromorphic computing program
The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
- 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_69a88a184cbc8190877791f6552c2484 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbeaeb58881908ad34f7b253bac2a |
completed | March 7, 2026, 5:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae58f511a08190880fbde8900d59df |
completed | March 9, 2026, 5:21 a.m. |
Created at: March 4, 2026, 7:45 p.m.