Triple

T8309979
Position Surface form Disambiguated ID Type / Status
Subject Region of Southern Denmark E194566 entity
Predicate containsCity P294 FINISHED
Object Vejle E211139 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: Vejle | Statement: [Region of Southern Denmark, containsCity, Vejle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vejle
Context triple: [Region of Southern Denmark, containsCity, Vejle]
  • A. Vejle chosen
    Vejle is a Danish city known for its scenic fjord setting, rolling hills, and role as a regional commercial and transportation hub in southeastern Jutland.
  • B. Holbæk
    Holbæk is a coastal town and municipality in northwestern Zealand, Denmark, known for its harbor on Holbæk Fjord and role as a regional commercial and cultural center.
  • C. Tønder
    Tønder is a historic market town in southern Denmark near the German border, known for its well-preserved old town and cultural heritage.
  • D. Kolding
    Kolding is a historic Danish city in Southern Jutland known for Koldinghus Castle, its fjord-side location, and its role as a regional cultural and educational center.
  • E. Slagelse
    Slagelse is a town on the island of Zealand in Denmark known for its military presence, historical significance, and role as a regional commercial center.
  • 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_69ca82e613e88190bf8139669bbd0d53 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7f2d2c30819095075940479b75a7 completed March 31, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69d046efb42c8190b19c8ecd5efa8956 completed April 3, 2026, 11:02 p.m.
Created at: March 30, 2026, 5:54 p.m.