Triple

T17560794
Position Surface form Disambiguated ID Type / Status
Subject GIN index E427691 entity
Predicate fullName P16 FINISHED
Object Generalized Inverted Index NE NERFINISHED

How this triple was built (3 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: Generalized Inverted Index | Statement: [GIN index, fullName, Generalized Inverted Index]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Generalized Inverted Index
Context triple: [GIN index, fullName, Generalized Inverted Index]
  • A. Introduction to Information Retrieval
    Introduction to Information Retrieval is a widely used academic textbook that provides a comprehensive foundation in the principles, algorithms, and evaluation methods of modern information retrieval and search engines.
  • B. 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.
  • C. COSMAS II corpus search system
    COSMAS II corpus search system is a large-scale linguistic search platform for German language text corpora, maintained by the Institut für Deutsche Sprache for research and lexicographic analysis.
  • 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. “Index, Context, and Content”
    “Index, Context, and Content” is a philosophical essay by David Lewis that analyzes how indexical expressions depend on context to determine their content and reference.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Generalized Inverted Index
Target entity description: Generalized Inverted Index is a PostgreSQL index type optimized for fast full-text search and complex queries on composite or array-like data structures.
  • A. Introduction to Information Retrieval
    Introduction to Information Retrieval is a widely used academic textbook that provides a comprehensive foundation in the principles, algorithms, and evaluation methods of modern information retrieval and search engines.
  • B. 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.
  • C. COSMAS II corpus search system
    COSMAS II corpus search system is a large-scale linguistic search platform for German language text corpora, maintained by the Institut für Deutsche Sprache for research and lexicographic analysis.
  • 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. “Index, Context, and Content”
    “Index, Context, and Content” is a philosophical essay by David Lewis that analyzes how indexical expressions depend on context to determine their content and reference.
  • F. None of above. chosen

Provenance (2 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_69d889e0385081908a04b66f4dd4bd0d completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e456267e208190a1238fbe1a535bb0 completed April 19, 2026, 4:12 a.m.
Created at: April 10, 2026, 5:50 a.m.