Triple

T8367952
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Ribe E197380 entity
Predicate hasCentralTown P1474 FINISHED
Object Ribe E728701 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: Ribe | Statement: [Diocese of Ribe, hasCentralTown, Ribe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ribe
Context triple: [Diocese of Ribe, hasCentralTown, Ribe]
  • A. Ribe chosen
    Ribe is the oldest town in Denmark, known for its well-preserved medieval center and Viking heritage.
  • B. Havelberg
    Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
  • C. Viborg
    Viborg is the Swedish name for the historic Karelian city of Vyborg, located near the Finnish border on the Gulf of Finland.
  • D. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • E. Rudkøbing
    Rudkøbing is a small historic town on the Danish island of Langeland, known for its well-preserved old streets and as the birthplace of physicist Hans Christian Ørsted.
  • 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_69ca82f56730819080cec5d991c76f4c completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb808e56fc81908b5d37482f29452d completed March 31, 2026, 8:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce1d0a3ba481909a8c247c4c476104 completed April 2, 2026, 7:38 a.m.
Created at: March 30, 2026, 6 p.m.