Triple

T21342064
Position Surface form Disambiguated ID Type / Status
Subject Skinner Park E526218 entity
Predicate partOf P40 FINISHED
Object San Fernando urban area — NE NERFINISHED

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: San Fernando urban area | Statement: [Skinner Park, partOf, San Fernando urban area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Fernando urban area
Context triple: [Skinner Park, partOf, San Fernando urban area]
  • A. San Fernando metropolitan area chosen
    The San Fernando metropolitan area is a major urban and commercial region in southern Trinidad centered on the city of San Fernando.
  • B. San Fernando
    San Fernando is a small Andean town in Ecuador’s Azuay Province, known for its rural highland landscapes and traditional agricultural communities.
  • C. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • D. San Fernando
    San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
  • E. San Fernando
    San Fernando is a municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51c33048190ab27cede74ef798c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8a84fa8088190afda63af7f4ce586 completed April 22, 2026, 10:51 a.m.
Created at: April 16, 2026, 4:44 p.m.