Triple

T2739841
Position Surface form Disambiguated ID Type / Status
Subject Tacuba E60721 entity
Predicate hasUrbanProblem P32029 FINISHED
Object traffic congestion 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: traffic congestion | Statement: [Tacuba, hasUrbanProblem, traffic congestion]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasUrbanProblem
Context triple: [Tacuba, hasUrbanProblem, traffic congestion]
  • A. hasUrbanIssue chosen
    Indicates that an entity experiences, is affected by, or is associated with a specific problem or challenge related to urban environments or city life.
  • B. 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.
  • C. 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.
  • D. hasUrbanRole
    Indicates that an entity plays a specific functional or social role within an urban or city context.
  • E. 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.
  • 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_69ab4b77febc819095603eb012cd141b completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb2da94c8190bc9d23262e3dfc07 completed March 7, 2026, 8 a.m.
PD Predicate disambiguation batch_69abd82859348190bce3be8f2e9d60ba completed March 7, 2026, 7:47 a.m.
Created at: March 6, 2026, 9:56 p.m.