Triple

T6125853
Position Surface form Disambiguated ID Type / Status
Subject What Makes a Family E136592 entity
Predicate setting P1957 FINISHED
Object Florida E549 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: Florida | Statement: [What Makes a Family, setting, Florida]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Florida
Context triple: [What Makes a Family, setting, Florida]
  • A. Florida chosen
    Florida is a southeastern U.S. state known for its warm climate, extensive beaches, tourism industry centered on attractions like Walt Disney World, and significant cultural and economic influence.
  • B. Florida
    Florida is a small rural town located in the northern part of Berkshire County, Massachusetts, known for its mountainous terrain and proximity to the Mohawk Trail.
  • C. Florida
    Florida is a residential suburb of Roodepoort in Gauteng, South Africa, known for its lakes, schools, and commuter access to Johannesburg.
  • D. La Florida
    La Florida was a vast 16th-century Spanish colonial territory in the southeastern region of what is now the United States, encompassing parts of present-day Florida and surrounding areas.
  • E. La Florida
    La Florida is a populous residential commune and suburb of Santiago in central Chile, known for its urban development and commercial activity.
  • 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_69c008a0a37c81908e5b4f879158afb3 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05c28dbbc8190a0a0c20ec794e81a completed March 22, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c135bd8d3881909873d2a063b3aecc completed March 23, 2026, 12:44 p.m.
Created at: March 22, 2026, 4:14 p.m.