Triple

T11903460
Position Surface form Disambiguated ID Type / Status
Subject Cristóbal Balenciaga E283214 entity
Predicate workLocation P7 FINISHED
Object San Sebastián E138087 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 Sebastián | Statement: [Cristóbal Balenciaga, workLocation, San Sebastián]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Sebastián
Context triple: [Cristóbal Balenciaga, workLocation, San Sebastián]
  • A. San Sebastián
    San Sebastián is a Guatemalan town located in the highlands of the San Marcos department, known for its proximity to Central America’s highest peak, Volcán Tajumulco.
  • B. Donostia-San Sebastián chosen
    Donostia-San Sebastián is a coastal city in Spain’s Basque Country renowned for its picturesque bay, beaches, and world-class gastronomy.
  • C. Bilbao
    Bilbao is a major port city in northern Spain renowned for its industrial heritage, cultural institutions like the Guggenheim Museum, and role as an economic hub of the Basque Country.
  • D. Bilbao
    Bilbao is a station on Madrid's Metro network, serving Line 1 and located in the central Chamberí district.
  • E. Pamplona
    Pamplona is a historic Colombian city in the Andean region known for its colonial architecture, religious heritage, and role as an educational and cultural center.
  • 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_69d6ab2c07e88190ba13b0d21fd6cf33 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd1792648190853f15fbf217eebd completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f4183d29b081908cfbf4d91a365681 completed May 1, 2026, 3:04 a.m.
Created at: April 8, 2026, 9:44 p.m.