Triple
T307338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DeepMind |
E6331
|
entity |
| Predicate | developed |
P73
|
FINISHED |
| Object |
AlphaGo Zero
AlphaGo Zero is an advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human game data.
|
E40166
|
NE FINISHED |
How this triple was built (4 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: AlphaGo Zero | Statement: [DeepMind, developed, AlphaGo Zero]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AlphaGo Zero Context triple: [DeepMind, developed, AlphaGo Zero]
-
A.
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.
-
B.
AlphaZero
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.
-
C.
DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
-
D.
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.
-
E.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: AlphaGo Zero Triple: [DeepMind, developed, AlphaGo Zero]
Generated description
AlphaGo Zero is an advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human game data.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: AlphaGo Zero Target entity description: AlphaGo Zero is an advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human game data.
-
A.
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.
-
B.
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.
-
C.
DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
-
D.
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.
-
E.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
- F. None of above.
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_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_69a3c8b480388190b5f7f9e11479de91 |
completed | March 1, 2026, 5:03 a.m. |
| NEDg | Description generation | batch_69a3c91e8b88819099e54b7869cc293d |
completed | March 1, 2026, 5:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3c9731df881908e342ace3635b5cc |
completed | March 1, 2026, 5:06 a.m. |
Created at: Feb. 28, 2026, 1:06 p.m.