Triple
T10023643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auto-Encoding Variational Bayes |
E200670
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Diederik P. Kingma |
E182823
|
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: Diederik P. Kingma | Statement: [Auto-Encoding Variational Bayes, author, Diederik P. Kingma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diederik P. Kingma Context triple: [Auto-Encoding Variational Bayes, author, Diederik P. Kingma]
-
A.
Diederik P. Kingma
chosen
Diederik P. Kingma is a machine learning researcher best known for co-developing the Adam optimization algorithm and the variational autoencoder (VAE) framework.
-
B.
Ilya Goodfellow
Ilya Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and contributing to deep learning at organizations like Google and OpenAI.
-
C.
Ian Goodfellow
Ian Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and co-authoring the influential textbook "Deep Learning."
-
D.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
E.
Nicolas Heess
Nicolas Heess is a machine learning researcher known for his work in deep reinforcement learning, including contributions to algorithms such as Deep Deterministic Policy Gradient (DDPG).
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd7c75548190aa604d90d63dc111 |
completed | April 2, 2026, 1:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d28222773c81908eb84974fd6ce106 |
completed | April 5, 2026, 3:39 p.m. |
Created at: March 30, 2026, 8:53 p.m.