Triple

T6509325
Position Surface form Disambiguated ID Type / Status
Subject University of Iceland E150087 entity
Predicate locatedIn P40 FINISHED
Object Suðvesturland
Suðvesturland is the southwestern region of Iceland that includes the capital area and serves as a central hub for the country’s education, culture, and administration.
 E601336 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: Suðvesturland | Statement: [University of Iceland, locatedIn, Suðvesturland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suðvesturland
Context triple: [University of Iceland, locatedIn, Suðvesturland]
  • A. Vestland
    Vestland is a county in western Norway known for its dramatic fjords, coastal landscapes, and the city of Bergen.
  • B. Nord
    Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
  • C. Suðurland
    Suðurland is a region in southern Iceland known for its dramatic landscapes, including waterfalls, glaciers, black sand beaches, and active volcanoes.
  • D. Norden
    Norden is a suburban village and residential area within the Metropolitan Borough of Rochdale in Greater Manchester, England.
  • E. Norden
    Norden is a historic coastal town in northern Germany’s East Frisia region, known for its North Sea proximity and traditional Frisian character.
  • 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: Suðvesturland
Triple: [University of Iceland, locatedIn, Suðvesturland]
Generated description
Suðvesturland is the southwestern region of Iceland that includes the capital area and serves as a central hub for the country’s education, culture, and administration.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suðvesturland
Target entity description: Suðvesturland is the southwestern region of Iceland that includes the capital area and serves as a central hub for the country’s education, culture, and administration.
  • A. Vestland
    Vestland is a county in western Norway known for its dramatic fjords, coastal landscapes, and the city of Bergen.
  • B. Nord
    Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
  • C. Suðurland
    Suðurland is a region in southern Iceland known for its dramatic landscapes, including waterfalls, glaciers, black sand beaches, and active volcanoes.
  • D. Norden
    Norden is a suburban village and residential area within the Metropolitan Borough of Rochdale in Greater Manchester, England.
  • E. Norden
    Norden is a historic coastal town in northern Germany’s East Frisia region, known for its North Sea proximity and traditional Frisian character.
  • 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_69c687ef291081909d437f035eef1cda completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c69f386aa08190bfc8592a92ec6339 completed March 27, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cb5782fc8190a56b714bbc007490 completed March 27, 2026, 6:24 p.m.
NEDg Description generation batch_69c6cd88f66c81909b364a816aeee8bf completed March 27, 2026, 6:33 p.m.
NED2 Entity disambiguation (via description) batch_69c6ce3a53cc8190a40d696a22ec65f4 completed March 27, 2026, 6:36 p.m.
Created at: March 27, 2026, 1:43 p.m.