Triple

T10813802
Position Surface form Disambiguated ID Type / Status
Subject FC Dallas Stadium E255171 entity
Predicate city P40 FINISHED
Object 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: Frisco | Statement: [FC Dallas Stadium, city, Frisco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frisco
Context triple: [FC Dallas Stadium, city, Frisco]
  • A. Frisco
    Frisco is the popular nickname for the historic St. Louis–San Francisco Railway, a major American railroad that operated across the Midwest and South.
  • B. 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.
  • C. 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.
  • D. San Antonio
    San Antonio is a coastal municipality in the province of Northern Samar in the Philippines, known for its island beaches and fishing communities.
  • E. San Antonio
    San Antonio is a coastal municipality in the Philippine province of Zambales known for its beaches, coves, and nearby island-hopping destinations.
  • 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_69d6aa8081448190a9324184f2bd1c26 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d733eba9b48190b4dbe7fe5d8be0a4 completed April 9, 2026, 5:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69e3c7d11cf081908f714686f582c081 completed April 18, 2026, 6:05 p.m.
Created at: April 8, 2026, 9:18 p.m.