Triple

T6642141
Position Surface form Disambiguated ID Type / Status
Subject Bernard Zakheim E150610 entity
Predicate workLocation P7 FINISHED
Object San Francisco E242 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: San Francisco | Statement: [Bernard Zakheim, workLocation, San Francisco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Francisco
Context triple: [Bernard Zakheim, workLocation, 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 Fransokyo
    San Fransokyo is a fictional futuristic hybrid city combining elements of San Francisco and Tokyo, serving as the primary setting of Disney's animated film "Big Hero 6."
  • C. Sausalito
    Sausalito is a picturesque waterfront city in Northern California known for its hillside homes, art galleries, and views of the San Francisco Bay.
  • D. San Jose
    San Jose is a coastal municipality in the Philippine province of Negros Oriental known for its rural communities and proximity to Dumaguete City.
  • E. San Jose
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • 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_69c687f1a3048190828b7342f7125d5c completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6aff5da8881909a512c1c82eb882a completed March 27, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f78896b48190a8d993c207d01a7e completed March 27, 2026, 9:32 p.m.
Created at: March 27, 2026, 2 p.m.