Triple

T4532417
Position Surface form Disambiguated ID Type / Status
Subject Things to Come E106326 entity
Predicate hasFictionalCity P21117 FINISHED
Object Everytown E450632 NE FINISHED

How this triple was built (3 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: Everytown | Statement: [Things to Come, hasFictionalCity, Everytown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Everytown
Context triple: [Things to Come, hasFictionalCity, Everytown]
  • A. Everytown chosen
    Everytown is a fictional futuristic British city depicted in the 1936 science fiction film "Things to Come," representing an idealized, technologically advanced society.
  • B. Red Town
    Red Town is a historical region associated with the settlement of Krasnaya Sloboda, known for its cultural and regional significance.
  • C. B-Town
    B-Town is a common nickname for Bloomington, Indiana, a vibrant Midwestern college city best known as the home of Indiana University.
  • D. O-Town
    O-Town is a popular nickname for the city of Orlando, Florida, often used in local culture and media.
  • E. O-Town
    O-Town is an American boy band formed on the reality TV series "Making the Band," known for early-2000s pop hits like "Liquid Dreams" and "All or Nothing."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasFictionalCity
Context triple: [Things to Come, hasFictionalCity, Everytown]
  • A. partOfFictionalCity
    Indicates that one entity is a component, area, or subdivision within a larger fictional city.
  • B. hasFictionalTownBasedOn
    Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
  • C. hasFictionalLocation chosen
    Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
  • D. basedInFictionalLocation
    Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
  • E. hasFictionalCountySeatRole
    Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
  • F. None of above.

Provenance (4 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd579f27ac8190ae9a4252109e56e1 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdb9193e2481908901b9b4eb307da8 completed March 20, 2026, 9:16 p.m.
PD Predicate disambiguation batch_69bd521edd00819099dfccaa65dddd61 completed March 20, 2026, 1:56 p.m.
Created at: March 20, 2026, 1:03 p.m.