Triple

T4555
Position Surface form Disambiguated ID Type / Status
Subject Turing Award E88 entity
Predicate sponsor P67 FINISHED
Object Google E1096 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: Google | Statement: [Turing Award, sponsor, Google]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Google
Context triple: [Turing Award, sponsor, Google]
  • A. Google chosen
    Google is a multinational technology company best known for its search engine and wide range of internet-related products and services, including Android, YouTube, and cloud computing.
  • B. IBM
    IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
  • C. World Wide Web
    The World Wide Web is a global system of interlinked hypertext documents and resources accessed via the internet, enabling users worldwide to browse, share, and interact with information through web browsers.
  • D. Mark Zuckerberg
    Mark Zuckerberg is an American technology entrepreneur and philanthropist best known as the co-founder and CEO of Facebook (now Meta Platforms).
  • E. Silicon Valley
    Silicon Valley is a globally renowned technology and innovation hub in Northern California, home to many of the world’s leading tech companies and startups.
  • 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_69a238d6b47881909e68288aed2fd858 completed Feb. 28, 2026, 12:37 a.m.
NER Named-entity recognition batch_69a2399c646c8190b4977ed56b8835a8 completed Feb. 28, 2026, 12:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69a248d31b848190bad943bca271fca0 completed Feb. 28, 2026, 1:45 a.m.
Created at: Feb. 28, 2026, 12:40 a.m.