Triple

T23336883
Position Surface form Disambiguated ID Type / Status
Subject Viana E591614 entity
Predicate near P350 FINISHED
Object Logroño 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: Logroño | Statement: [Viana, near, Logroño]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Logroño
Context triple: [Viana, near, Logroño]
  • A. Logroño chosen
    Logroño is the capital city of Spain’s La Rioja region, renowned for its historic old town, vibrant tapas culture, and role as a key stop on the Camino de Santiago pilgrimage route.
  • B. Mondoñedo
    Mondoñedo is a historic town in northwestern Spain, renowned for its medieval cathedral and former status as an important ecclesiastical and administrative center in the region of Galicia.
  • C. Burgos
    Burgos is a historic city in northern Spain known for its medieval architecture and its prominent role during the Spanish Civil War.
  • D. Burgos
    Burgos is a small coastal municipality on the northern tip of Siargao Island in the Philippines, known for its quiet beaches and surf spots.
  • E. Zaragosa
    Zaragosa is a barangay (village-level administrative division) within the municipality of Badian in the province of Cebu, Philippines.
  • 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_69e25d20156c81908c5c53195bd9c738 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1982f8574819090f8b0ba249237a3 completed April 29, 2026, 5:33 a.m.
Created at: April 17, 2026, 5:17 p.m.