Triple

T18764525
Position Surface form Disambiguated ID Type / Status
Subject Jylland E458858 entity
Predicate containsCity P294 FINISHED
Object Skagen NE NERFINISHED

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: Skagen | Statement: [Jylland, containsCity, Skagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Skagen
Context triple: [Jylland, containsCity, Skagen]
  • A. Skagen
    Skagen is a minimalist Danish-inspired watch and accessories brand known for its clean design aesthetic and modern, affordable timepieces.
  • B. Skagen chosen
    Skagen is Denmark’s northernmost town, renowned for its picturesque fishing harbor, distinctive yellow houses, and the scenic meeting point of the North Sea and Baltic Sea.
  • C. Swatch
    Swatch is a Swiss watchmaker best known for its colorful, affordable fashion watches that helped revitalize the Swiss watch industry in the 1980s.
  • D. Daniel Wellington
    Daniel Wellington is a Swedish watch brand known for its minimalist, fashion-oriented timepieces with interchangeable NATO and leather straps.
  • E. Tissot
    Tissot is a Swiss watchmaker renowned for its affordable yet high-quality timepieces and long heritage in traditional and sports-oriented horology.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d395dba0819087568404508590cb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e58d82297c81909f720e2637cec737 completed April 20, 2026, 2:20 a.m.
Created at: April 10, 2026, 11:52 a.m.