Triple

T13314184
Position Surface form Disambiguated ID Type / Status
Subject Ponent region E317147 entity
Predicate contains P35 FINISHED
Object Segarra E614164 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: Segarra | Statement: [Ponent region, contains, Segarra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Segarra
Context triple: [Ponent region, contains, Segarra]
  • A. Segarra chosen
    Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
  • B. Gandria
    Gandria is a picturesque lakeside village in southern Switzerland known for its historic stone houses, narrow alleyways, and scenic setting on the shores of Lake Lugano.
  • C. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • D. Gandesa
    Gandesa is a historic town in Catalonia, Spain, known for its wine production and role in the Battle of the Ebro during the Spanish Civil War.
  • E. Figaró-Montmany
    Figaró-Montmany is a small municipality in the province of Barcelona, Catalonia, Spain, situated in a mountainous area near the Montseny Natural Park.
  • 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_69d806b40ab4819094adf6c374f4811a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d990f8a86481909ea2942c63037b77 completed April 11, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69f794202df08190acf1a7710b64198c completed May 3, 2026, 6:29 p.m.
Created at: April 9, 2026, 9:29 p.m.