Triple

T7384856
Position Surface form Disambiguated ID Type / Status
Subject Wangcheng County E170354 entity
Predicate hasUrbanizationStatus P40854 FINISHED
Object rapidly urbanizing 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: rapidly urbanizing | Statement: [Wangcheng County, hasUrbanizationStatus, rapidly urbanizing]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasUrbanizationStatus
Context triple: [Wangcheng County, hasUrbanizationStatus, rapidly urbanizing]
  • A. isUrbanized
    Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
  • B. locatedInUrbanizationType
    Indicates that one entity is situated within, or belongs to, a specific type or category of urbanized area (e.g., city, suburb, metropolitan zone).
  • C. hasUrbanClassification chosen
    Indicates that an entity is assigned a specific urban status or category within a defined classification system.
  • D. isUrbanizing
    Indicates a process in which an area or population becomes more urban in character, typically through increased development, infrastructure, and concentration of people and activities.
  • E. urbanizationLevel
    Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
  • 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_69c68a5d0ed08190b6d361e68f813330 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f1efe1308190b96eefbff56140be completed March 27, 2026, 9:09 p.m.
PD Predicate disambiguation batch_69c6f0309cc88190b55d278969400294 completed March 27, 2026, 9:01 p.m.
Created at: March 27, 2026, 3:08 p.m.