Triple

T3241421
Position Surface form Disambiguated ID Type / Status
Subject Mickey Mouse E67971 entity
Predicate setting P1957 FINISHED
Object Toontown E173784 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: Toontown | Statement: [Mickey Mouse, setting, Toontown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toontown
Context triple: [Mickey Mouse, setting, Toontown]
  • A. Mickey's Toontown chosen
    Mickey's Toontown is a whimsical, cartoon-themed land in Disney parks where guests can explore the colorful homes and neighborhoods of Mickey Mouse and his friends.
  • B. Discoveryland
    Discoveryland is a retro-futuristic themed land at Disneyland Paris inspired by the visionary works of Jules Verne and classic science fiction.
  • C. Storybook Land
    Storybook Land is a fairy tale–themed amusement park in Aberdeen, South Dakota, featuring storybook characters, themed rides, and attractions for young children and families.
  • D. Nickelodeon Universe
    Nickelodeon Universe is a large indoor theme park themed around Nickelodeon characters and shows, featuring rides, attractions, and entertainment for families and children.
  • E. Sky Land
    Sky Land is a high-altitude, cloud-filled world in Super Mario Bros. 3 known for its vertical level design and airborne challenges.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef76d908190815bb456e366ee0a completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b27754492c819099bab9a2a3344561 completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.