Triple

T7488803
Position Surface form Disambiguated ID Type / Status
Subject Danish–German border E176949 entity
Predicate nearCity P350 FINISHED
Object Tønder E635019 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: Tønder | Statement: [Danish–German border, nearCity, Tønder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tønder
Context triple: [Danish–German border, nearCity, Tønder]
  • A. Tønder chosen
    Tønder is a historic market town in southern Denmark near the German border, known for its well-preserved old town and cultural heritage.
  • B. Vejle
    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.
  • C. Næstved
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • D. Hjørring
    Hjørring is a historic town in northern Denmark known as one of the oldest settlements in the Vendsyssel region and a local commercial and cultural center.
  • E. 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.
  • 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_69c69f24ac508190bb98fe927c0bd065 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f55abcd481909e42ca857fe46cd1 completed March 27, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c91b1532808190a0b85fa98ef24cfa completed March 29, 2026, 12:29 p.m.
Created at: March 27, 2026, 3:43 p.m.