Triple

T14377010
Position Surface form Disambiguated ID Type / Status
Subject Fårö E356500 entity
Predicate hasSettlement P1068 FINISHED
Object Fårösund
Fårösund is a small coastal locality in Sweden known as a former naval base and ferry port on the island of Gotland.
 E1095940 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: Fårösund | Statement: [Fårö, hasSettlement, Fårösund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fårösund
Context triple: [Fårö, hasSettlement, Fårösund]
  • A. Hvannasund
    Hvannasund is a small fishing village and municipality in the Faroe Islands, located on the island of Viðoy.
  • B. Oxelösund
    Oxelösund is a small coastal industrial town in eastern Sweden known for its port facilities and steel production.
  • C. Norderön
    Norderön is a Swedish island located in the large inland lake Storsjön in the province of Jämtland.
  • D. Brunnsviken
    Brunnsviken is a scenic bay in the Stockholm area of Sweden, known for its surrounding parks, recreational areas, and cultural landmarks.
  • E. Västanfjärd
    Västanfjärd is a former municipality in Southwest Finland that was incorporated into the larger municipality of Kemiönsaari.
  • 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: Fårösund
Triple: [Fårö, hasSettlement, Fårösund]
Generated description
Fårösund is a small coastal locality in Sweden known as a former naval base and ferry port on the island of Gotland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fårösund
Target entity description: Fårösund is a small coastal locality in Sweden known as a former naval base and ferry port on the island of Gotland.
  • A. Hvannasund
    Hvannasund is a small fishing village and municipality in the Faroe Islands, located on the island of Viðoy.
  • B. Oxelösund
    Oxelösund is a small coastal industrial town in eastern Sweden known for its port facilities and steel production.
  • C. Norderön
    Norderön is a Swedish island located in the large inland lake Storsjön in the province of Jämtland.
  • D. Brunnsviken
    Brunnsviken is a scenic bay in the Stockholm area of Sweden, known for its surrounding parks, recreational areas, and cultural landmarks.
  • E. Västanfjärd
    Västanfjärd is a former municipality in Southwest Finland that was incorporated into the larger municipality of Kemiönsaari.
  • 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_69d8279163a081908aec45c0e3f1e02f completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de900949fc81909be0da1734c46645 completed April 14, 2026, 7:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd4c5728fc819089ef3c7c34b10101 completed May 8, 2026, 2:37 a.m.
NEDg Description generation batch_69fd4e4bae188190a8d1c5b833d58cd8 completed May 8, 2026, 2:45 a.m.
NED2 Entity disambiguation (via description) batch_69fd4f5782b4819081d32dbef032ac61 completed May 8, 2026, 2:49 a.m.
Created at: April 10, 2026, 1:16 a.m.