Triple

T10657944
Position Surface form Disambiguated ID Type / Status
Subject Roeselare E251142 entity
Predicate hasTwinTown P919 FINISHED
Object Vigo E189114 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: Vigo | Statement: [Roeselare, hasTwinTown, Vigo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vigo
Context triple: [Roeselare, hasTwinTown, Vigo]
  • A. Vigo chosen
    Vigo is a major industrial and port city in northwestern Spain, known for its shipbuilding, fishing industry, and location on the Atlantic coast of Galicia.
  • B. Vigo
    Vigo is a money transfer service brand that facilitates international remittances, particularly for customers sending funds to Latin America and other global regions.
  • C. A Coruña
    A Coruña is a coastal city in northwestern Spain known for its historic lighthouse, the Tower of Hercules, and its role as an important cultural and economic center in the region.
  • D. Pontevedra
    Pontevedra is a coastal municipality in the province of Capiz in the Philippines, known for its fishing communities and agricultural economy.
  • E. Pontevedra
    Pontevedra is a coastal province in northwestern Spain known for its historic towns, Atlantic landscapes, and location within the autonomous community of Galicia.
  • 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_69d6e01643a88190abc7c16fd0f85e53 completed April 8, 2026, 11:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a8375bc8190a79c09ba2626ce50 completed April 10, 2026, 10:32 p.m.
Created at: April 8, 2026, 9:07 p.m.