Triple

T10645244
Position Surface form Disambiguated ID Type / Status
Subject Sant Pere, Santa Caterina i la Ribera E250817 entity
Predicate hasPart P35 FINISHED
Object La Ribera E299310 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: La Ribera | Statement: [Sant Pere, Santa Caterina i la Ribera, hasPart, La Ribera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Ribera
Context triple: [Sant Pere, Santa Caterina i la Ribera, hasPart, La Ribera]
  • A. La Ribera chosen
    La Ribera is a historic neighborhood in Barcelona known for its medieval streets, vibrant cultural scene, and notable attractions such as the Picasso Museum.
  • B. Ribera
    Ribera was a prominent 17th-century Spanish Baroque painter, known for his dramatic use of light and shadow and intense religious and genre scenes.
  • C. Ribeira
    Ribeira is a historic riverside district in Porto, Portugal, known for its narrow medieval streets, colorful buildings, and vibrant waterfront atmosphere along the Douro River.
  • D. Ribeira
    Ribeira is a coastal town and municipality in the autonomous community of Galicia in northwestern Spain, known for its fishing industry and Atlantic beaches.
  • E. Nervión River
    The Nervión River is a prominent river in northern Spain that flows through the Basque Country and passes by the city of Bilbao before reaching the Bay of Biscay.
  • 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_69d6aa5a4c4881908f39be6efe5981e5 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfe120908190ab91c38d57133739 completed April 8, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a580d388190aea5edadd4afc0d1 completed April 10, 2026, 10:31 p.m.
Created at: April 8, 2026, 9:05 p.m.