Triple
T1793068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlphaGo |
E39541
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | AlphaGo Lee |
E200548
|
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: AlphaGo Lee | Statement: [AlphaGo, notableWork, AlphaGo Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AlphaGo Lee Context triple: [AlphaGo, notableWork, AlphaGo Lee]
-
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.
AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
-
D.
Lee Sedol
chosen
Lee Sedol is a South Korean professional Go player renowned as one of the strongest players in history and for his landmark 2016 match against DeepMind's AlphaGo.
-
E.
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.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa653b02448190bc475bc22187f5b0 |
completed | March 6, 2026, 5:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf54330c81908046b519a0297760 |
completed | March 8, 2026, 6:26 p.m. |
Created at: March 4, 2026, 7:32 p.m.