Triple

T1037347
Position Surface form Disambiguated ID Type / Status
Subject Kemi E22392 entity
Predicate twinTown P1072 FINISHED
Object Ängelholm
Ängelholm is a coastal town in southern Sweden known for its sandy beaches, aviation museum, and scenic location at the mouth of the Rönne River.
 E122152 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: Ängelholm | Statement: [Kemi, twinTown, Ängelholm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ängelholm
Context triple: [Kemi, twinTown, Ängelholm]
  • A. Strömstad
    Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
  • B. Trollhättan
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • C. Jönköping
    Jönköping is a city in southern Sweden, located at the southern end of Lake Vättern and known as a regional commercial and logistical hub.
  • D. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • E. Linköping
    Linköping is a major city in southern Sweden known for its university, high-tech industry, and historic cathedral.
  • 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: Ängelholm
Triple: [Kemi, twinTown, Ängelholm]
Generated description
Ängelholm is a coastal town in southern Sweden known for its sandy beaches, aviation museum, and scenic location at the mouth of the Rönne River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ängelholm
Target entity description: Ängelholm is a coastal town in southern Sweden known for its sandy beaches, aviation museum, and scenic location at the mouth of the Rönne River.
  • A. Strömstad
    Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
  • B. Trollhättan
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • C. Jönköping
    Jönköping is a city in southern Sweden, located at the southern end of Lake Vättern and known as a regional commercial and logistical hub.
  • D. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • E. Linköping
    Linköping is a major city in southern Sweden known for its university, high-tech industry, and historic cathedral.
  • 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_69a493d848848190aed4011b34b2e8d3 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b82a1014819085bfc077e24c9742 completed March 1, 2026, 10:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bc378fc8190846d5ffce73371dd completed March 7, 2026, 2:52 p.m.
NEDg Description generation batch_69ac3df28858819091c594a9cb2aab07 completed March 7, 2026, 3:02 p.m.
NED2 Entity disambiguation (via description) batch_69ac3e5b716c8190b95fde14ee6c434a completed March 7, 2026, 3:03 p.m.
Created at: March 1, 2026, 7:41 p.m.