Triple

T542268
Position Surface form Disambiguated ID Type / Status
Subject Sergey Brin E12655 entity
Predicate notableWork P4 FINISHED
Object The Anatomy of a Large-Scale Hypertextual Web Search Engine
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
E68398 NE FINISHED

How this triple was built (4 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: The Anatomy of a Large-Scale Hypertextual Web Search Engine | Statement: [Sergey Brin, notableWork, The Anatomy of a Large-Scale Hypertextual Web Search Engine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Anatomy of a Large-Scale Hypertextual Web Search Engine
Context triple: [Sergey Brin, notableWork, The Anatomy of a Large-Scale Hypertextual Web Search Engine]
  • A. PageRank algorithm
    The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
  • B. Architecture of the World Wide Web, Volume One
    Architecture of the World Wide Web, Volume One is a W3C-authored technical document that defines the foundational principles and design of the Web’s architecture.
  • C. Finding: The Self-Describing Web
    "Finding: The Self-Describing Web" is a W3C Technical Architecture Group document that explains how web resources should carry or link to enough metadata and semantics to allow automated agents and humans to understand and use them without prior agreement.
  • D. Finding: Publishing and Linking on the Web
    "Finding: Publishing and Linking on the Web" is a W3C Technical Architecture Group document that provides best-practice guidance on how web resources should be published and linked to ensure durability, interoperability, and clarity on the Web.
  • E. ACM Transactions on the Web
    ACM Transactions on the Web is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research and developments related to web technologies and applications.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: The Anatomy of a Large-Scale Hypertextual Web Search Engine
Triple: [Sergey Brin, notableWork, The Anatomy of a Large-Scale Hypertextual Web Search Engine]
Generated description
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: The Anatomy of a Large-Scale Hypertextual Web Search Engine
Target entity description: "The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
  • A. PageRank algorithm
    The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
  • B. Architecture of the World Wide Web, Volume One
    Architecture of the World Wide Web, Volume One is a W3C-authored technical document that defines the foundational principles and design of the Web’s architecture.
  • C. Finding: The Self-Describing Web
    "Finding: The Self-Describing Web" is a W3C Technical Architecture Group document that explains how web resources should carry or link to enough metadata and semantics to allow automated agents and humans to understand and use them without prior agreement.
  • D. Finding: Publishing and Linking on the Web
    "Finding: Publishing and Linking on the Web" is a W3C Technical Architecture Group document that provides best-practice guidance on how web resources should be published and linked to ensure durability, interoperability, and clarity on the Web.
  • E. ACM Transactions on the Web
    ACM Transactions on the Web is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research and developments related to web technologies and applications.
  • F. None of above. chosen

Provenance (5 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_69a49334226c81908b0ea1689ef6aa3f completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49861195081909540eaf402a5401a completed March 1, 2026, 7:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4d0403b248190a94b44b6073c500b completed March 1, 2026, 11:48 p.m.
NEDg Description generation batch_69a4d0aefa00819087320c7df48d8998 completed March 1, 2026, 11:50 p.m.
NED2 Entity disambiguation (via description) batch_69a4d130c6f081909b628aeafd2fc319 completed March 1, 2026, 11:52 p.m.
Created at: March 1, 2026, 7:32 p.m.