Triple

T5634694
Position Surface form Disambiguated ID Type / Status
Subject Älvkarleby Municipality E147918 entity
Predicate seat P75 FINISHED
Object Skutskär
Skutskär is a locality in Uppsala County, Sweden, known historically for its pulp and paper industry.
 E538542 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: Skutskär | Statement: [Älvkarleby Municipality, seat, Skutskär]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Skutskär
Context triple: [Älvkarleby Municipality, seat, Skutskär]
  • A. Skärholmen
    Skärholmen is a suburban district in southwestern Stockholm, Sweden, known for its large shopping center and residential areas.
  • B. Västerljung
    Västerljung is a small locality in eastern Sweden situated within Trosa Municipality in Södermanland County.
  • C. Skarpö
    Skarpö is an island in the Stockholm archipelago of Sweden, situated within Vaxholm Municipality and known for its coastal scenery and residential character.
  • D. Rindö
    Rindö is an island in Sweden’s Stockholm archipelago, known for its coastal scenery and strategic location near the town of Vaxholm.
  • E. Nakkholmen
    Nakkholmen is a small inhabited island known for its traditional wooden cabins and recreational use, located in the Oslofjord near Oslo, Norway.
  • 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: Skutskär
Triple: [Älvkarleby Municipality, seat, Skutskär]
Generated description
Skutskär is a locality in Uppsala County, Sweden, known historically for its pulp and paper industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Skutskär
Target entity description: Skutskär is a locality in Uppsala County, Sweden, known historically for its pulp and paper industry.
  • A. Skärholmen
    Skärholmen is a suburban district in southwestern Stockholm, Sweden, known for its large shopping center and residential areas.
  • B. Västerljung
    Västerljung is a small locality in eastern Sweden situated within Trosa Municipality in Södermanland County.
  • C. Skarpö
    Skarpö is an island in the Stockholm archipelago of Sweden, situated within Vaxholm Municipality and known for its coastal scenery and residential character.
  • D. Rindö
    Rindö is an island in Sweden’s Stockholm archipelago, known for its coastal scenery and strategic location near the town of Vaxholm.
  • E. Nakkholmen
    Nakkholmen is a small inhabited island known for its traditional wooden cabins and recreational use, located in the Oslofjord near Oslo, Norway.
  • 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_69c00907bc8881909ed760d3ed73ef35 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0226118548190877793dadf6cacba completed March 22, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d6d2dfc8190a2eabb8beda04ee5 completed March 22, 2026, 8:13 p.m.
NEDg Description generation batch_69c04e8a92a0819091bad1fbef4a509b completed March 22, 2026, 8:18 p.m.
NED2 Entity disambiguation (via description) batch_69c056acf1f48190bf7324ae178adbb7 completed March 22, 2026, 8:53 p.m.
Created at: March 22, 2026, 3:41 p.m.