Triple

T8347544
Position Surface form Disambiguated ID Type / Status
Subject Orvieto E196077 entity
Predicate twinnedWith P1072 FINISHED
Object Ribe
Ribe is the oldest town in Denmark, known for its well-preserved medieval center and Viking heritage.
 E728701 NE FINISHED

How this triple was built (4 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: [Orvieto, twinnedWith, Ribe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ribe
Context triple: [Orvieto, twinnedWith, Ribe]
  • A. 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.
  • B. Viborg
    Viborg is the Swedish name for the historic Karelian city of Vyborg, located near the Finnish border on the Gulf of Finland.
  • C. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • D. 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.
  • E. Korsholm
    Korsholm is a coastal municipality in western Finland, known for its largely Swedish-speaking population and proximity to the city of Vaasa in the Ostrobothnia region.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ribe
Triple: [Orvieto, twinnedWith, Ribe]
Generated description
Ribe is the oldest town in Denmark, known for its well-preserved medieval center and Viking heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ribe
Target entity description: Ribe is the oldest town in Denmark, known for its well-preserved medieval center and Viking heritage.
  • A. 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.
  • B. Viborg
    Viborg is the Swedish name for the historic Karelian city of Vyborg, located near the Finnish border on the Gulf of Finland.
  • C. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • D. 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.
  • E. Korsholm
    Korsholm is a coastal municipality in western Finland, known for its largely Swedish-speaking population and proximity to the city of Vaasa in the Ostrobothnia region.
  • F. None of above. chosen

Provenance (5 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_69ca82edd63c8190b876b8465464c5fa completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb801588e881908ac0a291280ac0f8 completed March 31, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc745f33c8190a043aff437874391 completed April 2, 2026, 1:32 a.m.
NEDg Description generation batch_69cdcc8596888190867bb0f298b6fac1 completed April 2, 2026, 1:55 a.m.
NED2 Entity disambiguation (via description) batch_69cdd14de9408190a5522fbdbef4d748 completed April 2, 2026, 2:15 a.m.
Created at: March 30, 2026, 5:58 p.m.