Triple

T369380
Position Surface form Disambiguated ID Type / Status
Subject Jøssingfjord E8234 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Sogndalstrand
Sogndalstrand is a historic coastal village in southwestern Norway known for its well-preserved wooden buildings and picturesque harbor setting.
 E46828 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: Sogndalstrand | Statement: [Jøssingfjord, hasNearbySettlement, Sogndalstrand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sogndalstrand
Context triple: [Jøssingfjord, hasNearbySettlement, Sogndalstrand]
  • A. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • B. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • C. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • D. Lysgårdsbakken
    Lysgårdsbakken is a large ski jumping hill complex in Lillehammer, Norway, best known for hosting the ski jumping events of the 1994 Winter Olympics.
  • E. Jøssingfjord
    Jøssingfjord is a narrow fjord on the southwestern coast of Norway, historically notable as the site of the 1940 Altmark Incident during World War II.
  • 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: Sogndalstrand
Triple: [Jøssingfjord, hasNearbySettlement, Sogndalstrand]
Generated description
Sogndalstrand is a historic coastal village in southwestern Norway known for its well-preserved wooden buildings and picturesque harbor setting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sogndalstrand
Target entity description: Sogndalstrand is a historic coastal village in southwestern Norway known for its well-preserved wooden buildings and picturesque harbor setting.
  • A. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • B. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • C. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • D. Lysgårdsbakken
    Lysgårdsbakken is a large ski jumping hill complex in Lillehammer, Norway, best known for hosting the ski jumping events of the 1994 Winter Olympics.
  • E. Jøssingfjord
    Jøssingfjord is a narrow fjord on the southwestern coast of Norway, historically notable as the site of the 1940 Altmark Incident during World War II.
  • 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_69a2e7f2ec648190b42bc7db424f8109 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebfdb0608190b1794a871d0d237a completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3ecac7e048190a76c02c738599c61 completed March 1, 2026, 7:37 a.m.
NEDg Description generation batch_69a3ed24dd888190bc333e764c228250 completed March 1, 2026, 7:39 a.m.
NED2 Entity disambiguation (via description) batch_69a3eeb57a9481908fe2b62805495b15 completed March 1, 2026, 7:45 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.