Triple

T22087213
Position Surface form Disambiguated ID Type / Status
Subject Botos Lake E545810 entity
Predicate nearbyCity P350 FINISHED
Object San José — 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 José | Statement: [Botos Lake, nearbyCity, San José]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San José
Context triple: [Botos Lake, nearbyCity, San José]
  • A. San José
    San José is a station on the Buenos Aires Underground, part of the city’s rapid transit network in Argentina.
  • B. San José chosen
    San José is a lakeside town in Guatemala’s Petén region, known for its proximity to Mayan archaeological sites and its location on the shores of Lake Petén Itzá.
  • C. San José
    San José is a small coastal village in Spain’s Cabo de Gata-Níjar Natural Park, known for its picturesque beaches, whitewashed houses, and role as a gateway to the park’s protected landscapes.
  • D. San José
    San José is a major city in Northern California’s Silicon Valley, known as a hub for technology, innovation, and diverse communities.
  • E. San José
    San José is the largest city in Northern California’s Silicon Valley, known as a major technology and innovation hub.
  • 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_69e11e3523488190badd54b5d580c00d completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f128e2e3108190883199f272756e4e completed April 28, 2026, 9:38 p.m.
Created at: April 16, 2026, 8:29 p.m.