Triple

T1231592
Position Surface form Disambiguated ID Type / Status
Subject Faroe Islands E26453 entity
Predicate hasIsland P970 FINISHED
Object Nólsoy
Nólsoy is a small, sparsely populated island in the Faroe Islands known for its traditional village, rich birdlife, and proximity to the capital, Tórshavn.
 E152466 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: Nólsoy | Statement: [Faroe Islands, hasIsland, Nólsoy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nólsoy
Context triple: [Faroe Islands, hasIsland, Nólsoy]
  • A. Vágar
    Vágar is one of the main islands of the Faroe Islands, known for hosting the archipelago’s only airport and serving as a key transport hub.
  • B. Eivissa
    Eivissa is the Catalan name for Ibiza, a popular Mediterranean island in Spain’s Balearic archipelago known for its beaches and nightlife.
  • C. Gardar
    Gardar was the principal ecclesiastical and administrative center of the Norse settlements in medieval Greenland, serving as the seat of the bishopric.
  • D. Bjerknes
    Bjerknes is a Norwegian surname most notably associated with the influential family of physicists and meteorologists who helped found modern weather forecasting and climate science.
  • E. Fløya
    Fløya is a Norwegian women's football club based in Tromsø that competes in the country's league system.
  • 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: Nólsoy
Triple: [Faroe Islands, hasIsland, Nólsoy]
Generated description
Nólsoy is a small, sparsely populated island in the Faroe Islands known for its traditional village, rich birdlife, and proximity to the capital, Tórshavn.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nólsoy
Target entity description: Nólsoy is a small, sparsely populated island in the Faroe Islands known for its traditional village, rich birdlife, and proximity to the capital, Tórshavn.
  • A. Vágar
    Vágar is one of the main islands of the Faroe Islands, known for hosting the archipelago’s only airport and serving as a key transport hub.
  • B. Eivissa
    Eivissa is the Catalan name for Ibiza, a popular Mediterranean island in Spain’s Balearic archipelago known for its beaches and nightlife.
  • C. Gardar
    Gardar was the principal ecclesiastical and administrative center of the Norse settlements in medieval Greenland, serving as the seat of the bishopric.
  • D. Bjerknes
    Bjerknes is a Norwegian surname most notably associated with the influential family of physicists and meteorologists who helped found modern weather forecasting and climate science.
  • E. Fløya
    Fløya is a Norwegian women's football club based in Tromsø that competes in the country's league system.
  • 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_69a4948571c88190a9191e451e6035fd completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4be5a25348190a0665b6324c4d8f5 completed March 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf1a58248190a270ae5baa18d0d6 completed March 8, 2026, 12:13 a.m.
NEDg Description generation batch_69acc0ea3ee88190a7938f07d508ed9e completed March 8, 2026, 12:20 a.m.
NED2 Entity disambiguation (via description) batch_69acc13d2168819090bb9d68180b2699 completed March 8, 2026, 12:22 a.m.
Created at: March 1, 2026, 7:47 p.m.