Triple
T8577094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ioannis Antonoglou |
E203073
|
entity |
| Predicate | coAuthorOf |
P2389
|
FINISHED |
| Object | Mastering the game of Go without human knowledge |
E229130
|
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: Mastering the game of Go without human knowledge | Statement: [Ioannis Antonoglou, coAuthorOf, Mastering the game of Go without human knowledge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mastering the game of Go without human knowledge Context triple: [Ioannis Antonoglou, coAuthorOf, Mastering the game of Go without human knowledge]
-
A.
AlphaGo Zero
chosen
AlphaGo Zero is DeepMind's advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human data.
-
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.
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.
-
D.
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.
-
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.
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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea97787481909ebbaa45f59cbdaa |
completed | March 31, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea89550f481908a7ed45303b71731 |
completed | April 2, 2026, 5:34 p.m. |
Created at: March 30, 2026, 6:22 p.m.