Triple

T2222057
Position Surface form Disambiguated ID Type / Status
Subject Linköping E48161 entity
Predicate hasTwinTown P919 FINISHED
Object Reykjavík E28255 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: Reykjavík | Statement: [Linköping, hasTwinTown, Reykjavík]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reykjavík
Context triple: [Linköping, hasTwinTown, Reykjavík]
  • A. Reykjavík, Iceland chosen
    Reykjavík, Iceland is the capital and largest city of Iceland, known for its vibrant cultural scene, geothermal pools, and role as the country’s political and economic center.
  • B. Copenhagen
    Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
  • C. Tallinn
    Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
  • D. Grindavík
    Grindavík is a small Icelandic fishing town on the Reykjanes Peninsula, known for its proximity to the Blue Lagoon geothermal spa and recent volcanic activity in the surrounding area.
  • E. Faro
    Faro is a historic coastal city in southern Portugal that serves as the capital of the Algarve region and a major gateway for tourism.
  • 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_69a88aa1ee708190862c8c378c41e9eb completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc03bfdd48190bfb96ec3e41c22dc completed March 7, 2026, 6:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae65605fa481908ac5b9d837600626 completed March 9, 2026, 6:14 a.m.
Created at: March 4, 2026, 7:47 p.m.