Triple

T19791137
Position Surface form Disambiguated ID Type / Status
Subject Bay Area freeway system E475410 entity
Predicate connects P390 FINISHED
Object San Jose — 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 Jose | Statement: [Bay Area freeway system, connects, San Jose]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Jose
Context triple: [Bay Area freeway system, connects, San Jose]
  • A. San Jose chosen
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • B. 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.
  • C. San Jose
    San Jose is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • D. San Jose
    San Jose is a municipality in the province of Tarlac in the Central Luzon region of the Philippines, known for its predominantly agricultural economy.
  • E. San Jose
    San Jose is a municipality in the province of Batangas in the Philippines, known for its agricultural economy and rural 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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653c217bc819092c517b27ca22087 completed April 20, 2026, 4:26 p.m.
Created at: April 10, 2026, 1:49 p.m.