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.