Triple

T3687581
Position Surface form Disambiguated ID Type / Status
Subject Bømlo E78261 entity
Predicate hasSettlement P1068 FINISHED
Object Rubbestadneset
Rubbestadneset is a village in the municipality of Bømlo in Vestland county, on the western coast of Norway.
 E380396 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: Rubbestadneset | Statement: [Bømlo, hasSettlement, Rubbestadneset]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rubbestadneset
Context triple: [Bømlo, hasSettlement, Rubbestadneset]
  • A. Grebbestad
    Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
  • B. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Akersneset
    Akersneset is a headland in central Oslo, Norway, forming part of the waterfront area that includes the historic Akershus Fortress.
  • E. Kjelsås
    Kjelsås is a residential neighborhood in northern Oslo, Norway, known for its hilly terrain, proximity to Marka forest, and access to the city via tram and rail connections.
  • 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: Rubbestadneset
Triple: [Bømlo, hasSettlement, Rubbestadneset]
Generated description
Rubbestadneset is a village in the municipality of Bømlo in Vestland county, on the western coast of Norway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rubbestadneset
Target entity description: Rubbestadneset is a village in the municipality of Bømlo in Vestland county, on the western coast of Norway.
  • A. Grebbestad
    Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
  • B. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Akersneset
    Akersneset is a headland in central Oslo, Norway, forming part of the waterfront area that includes the historic Akershus Fortress.
  • E. Kjelsås
    Kjelsås is a residential neighborhood in northern Oslo, Norway, known for its hilly terrain, proximity to Marka forest, and access to the city via tram and rail connections.
  • 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_69ad85e285a081908f8cbfa9e2ed9b75 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc4c7e2bc81909356c8b0ed90feed completed March 8, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4c3c10f6481908feed99cddc56d47 completed March 14, 2026, 2:11 a.m.
NEDg Description generation batch_69b4c7bd861c8190a1a7887f6d7fd6de completed March 14, 2026, 2:28 a.m.
NED2 Entity disambiguation (via description) batch_69b4c83085488190a48c6e4786d275e2 completed March 14, 2026, 2:30 a.m.
Created at: March 8, 2026, 3:26 p.m.