Triple

T848190
Position Surface form Disambiguated ID Type / Status
Subject Scottsdale, Arizona E18322 entity
Predicate metropolitanAreaPopulationRankInUS P520 FINISHED
Object one of the largest metro areas 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: one of the largest metro areas | Statement: [Scottsdale, Arizona, metropolitanAreaPopulationRankInUS, one of the largest metro areas]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: metropolitanAreaPopulationRankInUS
Context triple: [Scottsdale, Arizona, metropolitanAreaPopulationRankInUS, one of the largest metro areas]
  • A. rankByPopulationInUS
    Indicates the relative ordering of entities based on the size of their populations within the United States.
  • B. metropolitanAreaPopulationApproximate
    Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
  • C. rankByPopulationInUnitedStates chosen
    Indicates the relative ordering of entities based on their population size within the United States.
  • D. areaRankInUS
    Indicates the relative position of an entity in a ranking of areas within the United States, based on its size.
  • E. largestMetropolitanArea
    Indicates that one entity is the largest metropolitan area associated with, contained within, or relevant to another entity, typically by population or spatial extent.
  • 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_69a4938b04208190b82e1df6b572c548 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac1e41748190a21cd1ce5ebcb8ff completed March 1, 2026, 9:14 p.m.
PD Predicate disambiguation batch_69a4aa807adc8190ad808a573cf8e923 completed March 1, 2026, 9:07 p.m.
Created at: March 1, 2026, 7:38 p.m.