Triple
T307343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DeepMind |
E6331
|
entity |
| Predicate | developed |
P73
|
FINISHED |
| Object | Deep Q-Network |
E39543
|
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: Deep Q-Network | Statement: [DeepMind, developed, Deep Q-Network]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deep Q-Network Context triple: [DeepMind, developed, Deep Q-Network]
-
A.
Atari deep Q-network
chosen
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
DRL
DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
-
D.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
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_69a2e79230508190b912ecb555aae17e |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea313be88190b4441f3ea41a99e2 |
completed | Feb. 28, 2026, 1:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3bc255b308190b3f92e81801e65eb |
completed | March 1, 2026, 4:10 a.m. |
Created at: Feb. 28, 2026, 1:06 p.m.