Triple
T1923049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlphaZero |
E40166
|
entity |
| Predicate | publicationTitle |
P33185
|
FINISHED |
| Object | A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play |
E40166
|
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: A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play | Statement: [AlphaZero, publicationTitle, A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play Context triple: [AlphaZero, publicationTitle, A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play]
-
A.
Atari deep Q-network
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.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
C.
Monte Carlo tree search
Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
-
D.
AlphaZero
chosen
AlphaZero is a DeepMind-developed artificial intelligence system that mastered complex games like chess, shogi, and Go through self-play reinforcement learning without human-crafted strategies.
-
E.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
- 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: publicationTitle Context triple: [AlphaZero, publicationTitle, A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play]
-
A.
publicationType
Indicates the specific category or format of a published work that characterizes how it is issued or presented.
-
B.
publishedIn
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.
-
C.
publishedAs
Indicates that an entity is released, issued, or made publicly available under a particular name, format, or identity.
-
D.
publicationCompleted
Indicates that the process of publishing an item (such as a document, work, or release) has been fully finished and made officially available.
-
E.
formerPublication
Indicates that an entity was previously published in, or associated as a publication with, another entity but is no longer currently so.
- F. None of above. chosen
Provenance (5 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb23459ac819088ded5bfac9d4aad |
completed | March 7, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3e6678881908d72de7e0f19a648 |
completed | March 8, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69abafed2ab481908920334e77b1021b |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb1b180e481908bbe893d6ba6208b |
completed | March 7, 2026, 5:03 a.m. |
Created at: March 4, 2026, 7:35 p.m.