Triple
T9500680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlphaGo Zero |
E229130
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Monte Carlo tree search |
E205830
|
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: Monte Carlo tree search | Statement: [AlphaGo Zero, basedOn, Monte Carlo tree search]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo tree search Context triple: [AlphaGo Zero, basedOn, Monte Carlo tree search]
-
A.
Monte Carlo tree search
chosen
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.
-
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.
AlphaGo Zero
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.
-
D.
Generalized Search Tree
Generalized Search Tree is a flexible, balanced tree data structure framework that supports building custom index types for complex data and queries, often used in database systems.
-
E.
alpha–beta pruning
Alpha–beta pruning is a search algorithm optimization that reduces the number of nodes evaluated in minimax-based game tree searches by eliminating branches that cannot affect the final decision.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd983c308c8190bde6858ac1ca8ea5 |
completed | April 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12d439c6881909832afcc1154bca8 |
completed | April 4, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:57 p.m.