Triple

T5729584
Position Surface form Disambiguated ID Type / Status
Subject Frogner E126347 entity
Predicate contains P35 FINISHED
Object Vika
Vika is a central neighborhood in Oslo, Norway, known for its waterfront location, cultural institutions, and proximity to the city’s business district.
 E545581 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: Vika | Statement: [Frogner, contains, Vika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vika
Context triple: [Frogner, contains, Vika]
  • A. Vladimira
    Vladimira is a feminine given name, primarily used in Slavic cultures, derived from the male name Vladimir.
  • B. Nadezhda
    Nadezhda is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and meaning "hope."
  • C. Volkova
    Volkova is a Russian surname commonly borne by individuals of Slavic origin, including notable figures in politics, arts, and sciences.
  • D. Vasilyeva
    Vasilyeva is a common Russian surname, typically the feminine form of Vasilyev, derived from the given name Vasily.
  • E. Varvara
    Varvara is the Slavic form of the female given name Barbara, commonly used in Russian and other Eastern European languages.
  • 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: Vika
Triple: [Frogner, contains, Vika]
Generated description
Vika is a central neighborhood in Oslo, Norway, known for its waterfront location, cultural institutions, and proximity to the city’s business district.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vika
Target entity description: Vika is a central neighborhood in Oslo, Norway, known for its waterfront location, cultural institutions, and proximity to the city’s business district.
  • A. Vladimira
    Vladimira is a feminine given name, primarily used in Slavic cultures, derived from the male name Vladimir.
  • B. Nadezhda
    Nadezhda is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and meaning "hope."
  • C. Volkova
    Volkova is a Russian surname commonly borne by individuals of Slavic origin, including notable figures in politics, arts, and sciences.
  • D. Vasilyeva
    Vasilyeva is a common Russian surname, typically the feminine form of Vasilyev, derived from the given name Vasily.
  • E. Varvara
    Varvara is the Slavic form of the female given name Barbara, commonly used in Russian and other Eastern European languages.
  • 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_69c0082f723881908ce8bb13a0c0f8b7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025303860819093e51f176babed71 completed March 22, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dffe45481909eb617e40c83bd14 completed March 22, 2026, 11:40 p.m.
NEDg Description generation batch_69c08e19e7a481909a75c883bead8a35 completed March 23, 2026, 12:49 a.m.
NED2 Entity disambiguation (via description) batch_69c08e8f59548190a0f938a259e48212 completed March 23, 2026, 12:51 a.m.
Created at: March 22, 2026, 3:47 p.m.