Triple

T1774252
Position Surface form Disambiguated ID Type / Status
Subject SimpleText E38941 entity
Predicate successor P78 FINISHED
Object TextEdit on Mac OS X E41446 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: TextEdit on Mac OS X | Statement: [SimpleText, successor, TextEdit on Mac OS X]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TextEdit on Mac OS X
Context triple: [SimpleText, successor, TextEdit on Mac OS X]
  • A. TextEdit chosen
    TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
  • B. MacWrite
    MacWrite was one of the first WYSIWYG word processors for the original Macintosh, showcasing the platform’s graphical user interface and desktop publishing capabilities.
  • C. Bravo text editor
    Bravo text editor was an early WYSIWYG word processing program developed at Xerox PARC that pioneered many modern graphical text-editing concepts.
  • D. macOS Cocoa
    macOS Cocoa is Apple’s native object-oriented application framework for building graphical user interfaces on macOS.
  • E. Gypsy text editor
    Gypsy text editor was an early WYSIWYG word processing program for the Xerox Alto that pioneered modern graphical user interface concepts such as direct manipulation and mouse-based text editing.
  • 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_69a8862e61708190af97b9838cc3f5de completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64b59428819082e0d43a61f4f299 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada9982d208190b0c29ee1141e91b0 completed March 8, 2026, 4:53 p.m.
Created at: March 4, 2026, 7:31 p.m.