Triple

T962373
Position Surface form Disambiguated ID Type / Status
Subject Butler County E20762 entity
Predicate hasUrbanizationLevel P9969 FINISHED
Object partially suburbanized LITERAL 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: partially suburbanized | Statement: [Butler County, hasUrbanizationLevel, partially suburbanized]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasUrbanizationLevel
Context triple: [Butler County, hasUrbanizationLevel, partially suburbanized]
  • A. urbanizationLevel chosen
    Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
  • B. isUrbanized
    Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
  • C. hasUrbanFunction
    Indicates that an entity serves a specific role or purpose within an urban context, such as providing services, infrastructure, or activities typical of a city environment.
  • D. containsUrbanArea
    Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
  • E. hasUrbanFeature
    Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
  • F. None of above.

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_69a493b21f2881908132dcf45dcd2f36 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b415ac688190bbcef455935a3116 completed March 1, 2026, 9:48 p.m.
PD Predicate disambiguation batch_69a4b2a2e23c8190b932fe88b02f995d completed March 1, 2026, 9:41 p.m.
Created at: March 1, 2026, 7:40 p.m.