Triple

T6693585
Position Surface form Disambiguated ID Type / Status
Subject Dallas urban area E152689 entity
Predicate hasSuburb P747 FINISHED
Object McKinney E108484 NE 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: McKinney | Statement: [Dallas urban area, hasSuburb, McKinney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: McKinney
Context triple: [Dallas urban area, hasSuburb, McKinney]
  • A. McKinney
    McKinney is a surname of Scottish and Irish origin borne by various notable individuals, including American politician and activist Cynthia McKinney.
  • B. McKinney, Texas chosen
    McKinney, Texas is a rapidly growing suburban city in North Texas known for its historic downtown and high quality of life within the greater Dallas–Fort Worth area.
  • C. Forney
    Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
  • D. Wimberley
    Wimberley is a small, scenic town in central Texas known for its picturesque Hill Country landscapes, swimming holes, and artsy, tourist-friendly downtown.
  • E. Grand Prairie
    Grand Prairie is a mid-sized suburban city in the Dallas–Fort Worth metropolitan area known for its family attractions, parks, and growing residential communities.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69c6880687b08190805278b504d1c92c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6b1955e448190adbfed7dc28f8c52 completed March 27, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f7b97210819086e88624c476fa24 completed March 27, 2026, 9:33 p.m.
Created at: March 27, 2026, 2:05 p.m.