Triple

T13197078
Position Surface form Disambiguated ID Type / Status
Subject Diane Nguyen E314139 entity
Predicate worksFor P5820 FINISHED
Object VIM
VIM is a company that employs Diane Nguyen, likely operating in a professional or corporate services field.
E1026313 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: VIM | Statement: [Diane Nguyen, worksFor, VIM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VIM
Context triple: [Diane Nguyen, worksFor, VIM]
  • A. Vim
    Vim is a highly configurable, keyboard-driven text editor renowned for its efficiency, modal editing, and extensive plugin ecosystem, widely used by programmers and power users.
  • B. Neovim
    Neovim is a modern, extensible, and highly configurable fork of the Vim text editor, designed to improve usability, maintainability, and plugin integration for developers.
  • C. MacVim
    MacVim is a macOS-native graphical version of the Vim text editor that integrates with the Mac user interface while preserving Vim’s modal editing features.
  • D. vi text editor
    The vi text editor is a classic, modal, screen-oriented text editor for Unix systems that became a standard tool for programmers and system administrators.
  • E. Sublime Text
    Sublime Text is a fast, cross-platform, extensible text editor popular among developers for its powerful features and plugin ecosystem.
  • 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: VIM
Triple: [Diane Nguyen, worksFor, VIM]
Generated description
VIM is a company that employs Diane Nguyen, likely operating in a professional or corporate services field.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: VIM
Target entity description: VIM is a company that employs Diane Nguyen, likely operating in a professional or corporate services field.
  • A. Vim
    Vim is a highly configurable, keyboard-driven text editor renowned for its efficiency, modal editing, and extensive plugin ecosystem, widely used by programmers and power users.
  • B. Neovim
    Neovim is a modern, extensible, and highly configurable fork of the Vim text editor, designed to improve usability, maintainability, and plugin integration for developers.
  • C. MacVim
    MacVim is a macOS-native graphical version of the Vim text editor that integrates with the Mac user interface while preserving Vim’s modal editing features.
  • D. vi text editor
    The vi text editor is a classic, modal, screen-oriented text editor for Unix systems that became a standard tool for programmers and system administrators.
  • E. Sublime Text
    Sublime Text is a fast, cross-platform, extensible text editor popular among developers for its powerful features and plugin ecosystem.
  • 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_69d806ae1e08819090d95bfe1538cc17 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c626058819086f604b11af2d4eb completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6f605a48c81909373fcd9dd896b3d completed May 3, 2026, 7:15 a.m.
NEDg Description generation batch_69f6f6e10f2481909b405169dd7e5cf9 completed May 3, 2026, 7:18 a.m.
NED2 Entity disambiguation (via description) batch_69f6f73b301881909d792dfebd2e468f completed May 3, 2026, 7:20 a.m.
Created at: April 9, 2026, 9:16 p.m.