Triple

T8715786
Position Surface form Disambiguated ID Type / Status
Subject Vagn Walfrid Ekman E206890 entity
Predicate givenName P17 FINISHED
Object Vagn
Vagn is a Scandinavian given name, historically used in Denmark and other Nordic countries.
 E753936 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: Vagn | Statement: [Vagn Walfrid Ekman, givenName, Vagn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vagn
Context triple: [Vagn Walfrid Ekman, givenName, Vagn]
  • A. Vagnhärad
    Vagnhärad is a small locality in eastern Sweden known for its residential character and proximity to both the Baltic Sea coast and the Stockholm region.
  • B. Neoplan
    Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
  • C. Gaddi
    Gaddi is an Indo-Aryan language spoken primarily by the Gaddi people in the Himalayan regions of northern India, especially in Himachal Pradesh.
  • D. Bilen
    Bilen is a Cushitic language spoken primarily by the Bilen people in central Eritrea.
  • E. Vomag
    Vomag was a German vehicle manufacturer best known for producing military trucks and armored vehicles, including variants of the Panzer IV, during the World War II era.
  • 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: Vagn
Triple: [Vagn Walfrid Ekman, givenName, Vagn]
Generated description
Vagn is a Scandinavian given name, historically used in Denmark and other Nordic countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vagn
Target entity description: Vagn is a Scandinavian given name, historically used in Denmark and other Nordic countries.
  • A. Vagnhärad
    Vagnhärad is a small locality in eastern Sweden known for its residential character and proximity to both the Baltic Sea coast and the Stockholm region.
  • B. Neoplan
    Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
  • C. Gaddi
    Gaddi is an Indo-Aryan language spoken primarily by the Gaddi people in the Himalayan regions of northern India, especially in Himachal Pradesh.
  • D. Bilen
    Bilen is a Cushitic language spoken primarily by the Bilen people in central Eritrea.
  • E. Vomag
    Vomag was a German vehicle manufacturer best known for producing military trucks and armored vehicles, including variants of the Panzer IV, during the World War II era.
  • 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_69ca83572d4881909bef3be2b578d539 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5cd807cc819090b58caf285b397a completed March 31, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf28d62af88190acf2d8692d73b9f5 completed April 3, 2026, 2:41 a.m.
NEDg Description generation batch_69cf2bd222b08190907ba7e98991996e completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2fcb5e7c819086b441d1ef4fc368 completed April 3, 2026, 3:11 a.m.
Created at: March 30, 2026, 6:35 p.m.