Triple

T1616330
Position Surface form Disambiguated ID Type / Status
Subject Würzburg E34725 entity
Predicate hasTwinTown P919 FINISHED
Object Braga E167414 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: Braga | Statement: [Würzburg, hasTwinTown, Braga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Braga
Context triple: [Würzburg, hasTwinTown, Braga]
  • A. Braga chosen
    Braga is a historic city in northern Portugal known for its rich religious heritage, baroque architecture, and status as a regional cultural and educational center.
  • B. Guimarães
    Guimarães is a historic city in northern Portugal often regarded as the birthplace of the Portuguese nation and known for its well-preserved medieval center.
  • C. Coimbra
    Coimbra is a historic Portuguese city known for its medieval architecture and the University of Coimbra, one of the oldest universities in continuous operation in the world.
  • D. Porto
    Porto is Portugal’s second-largest city, renowned for its historic riverside district, rich maritime heritage, and production of port wine.
  • E. Almada
    Almada is a Portuguese city located on the south bank of the Tagus River, opposite Lisbon, known for its panoramic views of the capital and its prominent Cristo Rei statue.
  • 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_69a885ffc5ec819091afa325d5f9611c completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9099049e0819099763ecb09fb4f57 completed March 5, 2026, 4:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58c6d7e88190b9fc0e34a007a2f5 completed March 8, 2026, 11:08 a.m.
Created at: March 4, 2026, 7:28 p.m.