Triple

T6602192
Position Surface form Disambiguated ID Type / Status
Subject Ulrik Christian Gyldenløve E149025 entity
Predicate title P38 FINISHED
Object Count of Samsø
Count of Samsø is a Danish noble title historically associated with high-ranking aristocrats closely connected to the royal family.
 E606786 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: Count of Samsø | Statement: [Ulrik Christian Gyldenløve, title, Count of Samsø]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Count of Samsø
Context triple: [Ulrik Christian Gyldenløve, title, Count of Samsø]
  • A. Sundbyøster
    Sundbyøster is a district of Copenhagen located on the island of Amager, known primarily as a residential urban area.
  • B. Ginnerup
    Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
  • C. Nesodden
    Nesodden is a municipality and peninsula in southeastern Norway, situated across the Oslofjord from the capital city of Oslo.
  • D. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • E. Tysvær
    Tysvær is a coastal municipality in southwestern Norway known for its fjords, islands, and location between the cities of Haugesund and Stavanger.
  • 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: Count of Samsø
Triple: [Ulrik Christian Gyldenløve, title, Count of Samsø]
Generated description
Count of Samsø is a Danish noble title historically associated with high-ranking aristocrats closely connected to the royal family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Count of Samsø
Target entity description: Count of Samsø is a Danish noble title historically associated with high-ranking aristocrats closely connected to the royal family.
  • A. Sundbyøster
    Sundbyøster is a district of Copenhagen located on the island of Amager, known primarily as a residential urban area.
  • B. Ginnerup
    Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
  • C. Nesodden
    Nesodden is a municipality and peninsula in southeastern Norway, situated across the Oslofjord from the capital city of Oslo.
  • D. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • E. Tysvær
    Tysvær is a coastal municipality in southwestern Norway known for its fjords, islands, and location between the cities of Haugesund and Stavanger.
  • 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_69c687eaa7508190bb58ce2aa02039b3 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af10303081909541a140f8898979 completed March 27, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e434afd08190807faf0069c70cce completed March 27, 2026, 8:10 p.m.
NEDg Description generation batch_69c6e57d71ec8190b79615f11eadec26 completed March 27, 2026, 8:15 p.m.
NED2 Entity disambiguation (via description) batch_69c6e614f04c8190b25b553553895799 completed March 27, 2026, 8:18 p.m.
Created at: March 27, 2026, 1:56 p.m.