Triple

T2408272
Position Surface form Disambiguated ID Type / Status
Subject The Star in Frisco E50325 entity
Predicate developer P73 FINISHED
Object City of Frisco E22352 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: City of Frisco | Statement: [The Star in Frisco, developer, City of Frisco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Frisco
Context triple: [The Star in Frisco, developer, City of Frisco]
  • A. Frisco, Texas chosen
    Frisco, Texas is a rapidly growing suburban city in the Dallas–Fort Worth metropolitan area known for its sports venues, retail centers, and family-friendly communities.
  • B. Forney
    Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
  • C. Cowtown
    Cowtown is a popular nickname for Calgary, a major Canadian city known for its historic cattle industry and annual Calgary Stampede.
  • 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_69a88b0339a88190a1207333cd271cc9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc92408308190ad2d331ebee71d15 completed March 7, 2026, 6:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3eba9d08190a2c63e590e08b4df completed March 9, 2026, 11:50 a.m.
Created at: March 4, 2026, 7:58 p.m.