Triple

T17598554
Position Surface form Disambiguated ID Type / Status
Subject Tkinter E428634 entity
Predicate hasModule P12988 FINISHED
Object tkinter NE NERFINISHED

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: tkinter | Statement: [Tkinter, hasModule, tkinter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: tkinter
Context triple: [Tkinter, hasModule, tkinter]
  • A. Tkinter chosen
    Tkinter is Python’s standard GUI toolkit, providing a simple interface to the Tk GUI library for building desktop applications.
  • B. Tk
    Tk is a cross-platform graphical user interface (GUI) toolkit commonly used with the Tcl scripting language to build windowed applications.
  • C. TkAgg
    TkAgg is a Matplotlib backend that renders plots using the Agg engine and displays them in GUI windows via the Tkinter toolkit.
  • D. GTK
    GTK is a widely used open-source toolkit for creating graphical user interfaces, best known for powering applications in the GNOME desktop environment.
  • E. Tcl
    Tcl (Tool Command Language) is a high-level, embeddable scripting language widely used for rapid prototyping, GUI development (often with Tk), and extending applications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46c474e5481909d2736241b592dab completed April 19, 2026, 5:46 a.m.
Created at: April 10, 2026, 5:51 a.m.