Triple

T16060341
Position Surface form Disambiguated ID Type / Status
Subject Tivissa E389591 entity
Predicate partOf P40 FINISHED
Object Tarragona vegueria E80788 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: Tarragona vegueria | Statement: [Tivissa, partOf, Tarragona vegueria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tarragona vegueria
Context triple: [Tivissa, partOf, Tarragona vegueria]
  • A. Tarragona chosen
    Tarragona is a historic port city in northeastern Spain, renowned for its well-preserved Roman ruins and status as a major cultural and economic center in Catalonia.
  • B. Tarragona
    Tarragona is a coastal municipality in the province of Davao Oriental on the southeastern island of Mindanao in the Philippines.
  • C. Martorell
    Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
  • D. Begur
    Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
  • E. La Cava
    La Cava is a surname most notably associated with American film director Gregory La Cava, known for his influential work in early 20th-century cinema.
  • 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_69d86dae698881908327ef2d67706cb9 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1837850288190910ef37d6484c600 completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbe88a608190bc0a0cbfdb71e81d completed May 10, 2026, 1:14 a.m.
Created at: April 10, 2026, 4:57 a.m.