Triple

T1793091
Position Surface form Disambiguated ID Type / Status
Subject AlphaGo E39541 entity
Predicate alsoKnownAs P39 FINISHED
Object AlphaGo Lee E39541 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, alsoKnownAs, AlphaGo Lee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AlphaGo Lee
Context triple: [AlphaGo, alsoKnownAs, AlphaGo Lee]
  • A. AlphaGo chosen
    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
    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. Ke Jie
    Ke Jie is a Chinese professional Go player widely regarded as one of the strongest players of his generation.
  • 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_69adead0fb988190b403f5c62cbe991a completed March 8, 2026, 9:32 p.m.
Created at: March 4, 2026, 7:32 p.m.