Triple

T5503981
Position Surface form Disambiguated ID Type / Status
Subject Trosa Municipality E144394 entity
Predicate hasSeat P3522 FINISHED
Object Trosa
Trosa is a small coastal town in Södermanland County, Sweden, known for its picturesque wooden houses, harbor, and tourism.
 E530692 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: Trosa | Statement: [Trosa Municipality, hasSeat, Trosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Trosa
Context triple: [Trosa Municipality, hasSeat, Trosa]
  • A. Grimstad
    Grimstad is a coastal town and municipality in southern Norway known for its maritime heritage, charming wooden houses, and role as a summer tourist destination.
  • B. Tjeldsund
    Tjeldsund is a coastal municipality in northern Norway known for its location around the Tjeldsundet strait and its mix of island and mainland landscapes.
  • C. Sandvika
    Sandvika is a town in southeastern Norway that serves as the administrative center of Bærum and a commercial hub in the Greater Oslo Region.
  • D. Skudeneshavn
    Skudeneshavn is a historic coastal town in southwestern Norway known for its well-preserved wooden architecture and maritime heritage.
  • E. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • 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: Trosa
Triple: [Trosa Municipality, hasSeat, Trosa]
Generated description
Trosa is a small coastal town in Södermanland County, Sweden, known for its picturesque wooden houses, harbor, and tourism.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Trosa
Target entity description: Trosa is a small coastal town in Södermanland County, Sweden, known for its picturesque wooden houses, harbor, and tourism.
  • A. Grimstad
    Grimstad is a coastal town and municipality in southern Norway known for its maritime heritage, charming wooden houses, and role as a summer tourist destination.
  • B. Tjeldsund
    Tjeldsund is a coastal municipality in northern Norway known for its location around the Tjeldsundet strait and its mix of island and mainland landscapes.
  • C. Sandvika
    Sandvika is a town in southeastern Norway that serves as the administrative center of Bærum and a commercial hub in the Greater Oslo Region.
  • D. Skudeneshavn
    Skudeneshavn is a historic coastal town in southwestern Norway known for its well-preserved wooden architecture and maritime heritage.
  • E. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • 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_69c008f6b5048190a09064116062cf69 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01f0d21848190ae8c41561eca6342 completed March 22, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c027aa65608190a89fdfb0da675d4d completed March 22, 2026, 5:32 p.m.
NEDg Description generation batch_69c037b4e04881908d07e704f2a161bb completed March 22, 2026, 6:40 p.m.
NED2 Entity disambiguation (via description) batch_69c0384d023081909cb0d4ba4b80c07e completed March 22, 2026, 6:43 p.m.
Created at: March 22, 2026, 3:32 p.m.