Triple

T7137649
Position Surface form Disambiguated ID Type / Status
Subject Toyland E166348 entity
Predicate contains P35 FINISHED
Object Toy Town E166349 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: Toy Town | Statement: [Toyland, contains, Toy Town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toy Town
Context triple: [Toyland, contains, Toy Town]
  • A. Toy Town chosen
    Toy Town is the colorful, whimsical village setting in Enid Blyton’s Noddy stories, inhabited by living toys and other playful characters.
  • B. Little Town
    Little Town is a small hamlet in the Newlands Valley of England’s Lake District, known as a starting point for popular fell walks and its picturesque rural setting.
  • C. Township of Tiny
    The Township of Tiny is a rural municipality in central Ontario, Canada, known for its scenic Georgian Bay shoreline, cottages, and natural landscapes.
  • D. Magic Town
    Magic Town is a 1947 American comedy film starring James Stewart as a pollster who exploits a statistically average small town, directed by William A. Wellman and written by Robert Riskin.
  • E. Mob Town
    Mob Town is a historic nickname for the city of Baltimore, reflecting its long-standing reputation for civil unrest and rowdy public gatherings in the 19th century.
  • 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_69c68884a9388190af42f90d1c1a7151 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e6939b788190929e92ff481f2ee4 completed March 27, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7ad940bd88190abec876e2d2369bf completed March 28, 2026, 10:29 a.m.
Created at: March 27, 2026, 2:45 p.m.