Triple

T24335912
Position Surface form Disambiguated ID Type / Status
Subject French Mandate architecture E613379 entity
Predicate appliedInUrbanPlanning P155755 FINISHED
Object boulevard planning 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: boulevard planning | Statement: [French Mandate architecture, appliedInUrbanPlanning, boulevard planning]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: appliedInUrbanPlanning
Context triple: [French Mandate architecture, appliedInUrbanPlanning, boulevard planning]
  • A. hasUrbanPlanning
    Indicates that an entity is involved in, responsible for, or characterized by activities or attributes related to urban planning.
  • B. hasUrbanPlanner
    Indicates that an entity is associated with or utilizes a specific urban planner responsible for planning or managing its urban development.
  • C. mainUrbanPlan
    Indicates the primary urban planning scheme or framework that governs the development and organization of a given area.
  • D. landUsePlanning
    Indicates the relationship in which an authority or agent organizes, regulates, or designates how land and space may be used or developed within a given area.
  • E. urbanDesign
    Indicates the relationship in which an entity is responsible for planning, organizing, or shaping the physical layout and functional structure of urban spaces.
  • F. None of above. chosen

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_69e2d7dcc5a08190b53691130d56cbc4 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f292f5346881909ca93b7ceef543ed completed April 29, 2026, 11:23 p.m.
PD Predicate disambiguation batch_69f287ad30048190b3ad3613486f277f completed April 29, 2026, 10:35 p.m.
PDg Predicate description generation batch_69f28b7ff1808190870dfe9af789a1eb completed April 29, 2026, 10:51 p.m.
Created at: April 18, 2026, 1:56 a.m.