Triple

T18226948
Position Surface form Disambiguated ID Type / Status
Subject FactSet E436444 entity
Predicate hasOfficeIn P1268 FINISHED
Object San Francisco 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 Francisco | Statement: [FactSet, hasOfficeIn, San Francisco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Francisco
Context triple: [FactSet, hasOfficeIn, San Francisco]
  • A. San Francisco chosen
    San Francisco is a major coastal city in Northern California known for its hilly landscape, iconic Golden Gate Bridge, and role as a historic center of technology and counterculture.
  • B. San Francisco
    San Francisco is a coastal neighborhood of the city of Telde in Gran Canaria, Spain, known for its traditional Canarian architecture and historic character.
  • C. San Francisco
    San Francisco is a municipality in Colombia’s Putumayo Department, located in the southwestern part of the country near the Andean and Amazonian regions.
  • D. San Francisco
    San Francisco is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and proximity to the Bohol Sea.
  • E. San Francisco
    San Francisco is a coastal municipality in the Philippine province of Surigao del Norte, known for its island landscapes and fishing communities.
  • 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_69d8b9103a8081908bbb0836fef10efd completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4f4aff8748190be5e732f9dbc8dff completed April 19, 2026, 3:28 p.m.
Created at: April 10, 2026, 10:32 a.m.