Triple

T8596462
Position Surface form Disambiguated ID Type / Status
Subject Eskilstuna E203558 entity
Predicate hasTwinTown P919 FINISHED
Object Kolding E119252 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: Kolding | Statement: [Eskilstuna, hasTwinTown, Kolding]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kolding
Context triple: [Eskilstuna, hasTwinTown, Kolding]
  • A. Kolding chosen
    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.
  • B. Esbjerg
    Esbjerg is a major Danish port city on the North Sea, known for its offshore oil and wind industry, maritime heritage, and role as a regional economic center in western Jutland.
  • C. Aalborg
    Aalborg is a major city in northern Denmark known for its historic architecture, vibrant cultural life, and role as a regional economic and educational center.
  • D. Randers
    Randers is a historic market town and one of the largest cities in eastern Jutland, Denmark, known for its old town center and location along the Gudenå River.
  • E. Horsens
    Horsens is a city in eastern Jutland, Denmark, known historically as a market and industrial town and as the birthplace of explorer Vitus Bering.
  • 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_69ca832a7f108190b4e4f5648abf4aa2 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc46c945dc8190a313c61c0db46187 completed March 31, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d110045e6c8190b6ef9db10b5f688e completed April 4, 2026, 1:20 p.m.
Created at: March 30, 2026, 6:23 p.m.