Triple

T2877740
Position Surface form Disambiguated ID Type / Status
Subject Lübeck E56916 entity
Predicate twinCity P1072 FINISHED
Object Växjö
Växjö is a city in southern Sweden known for its lakeside setting, environmental sustainability initiatives, and role as a regional cultural and educational center.
 E328762 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: Växjö | Statement: [Lübeck, twinCity, Växjö]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Växjö
Context triple: [Lübeck, twinCity, Växjö]
  • A. Skövde
    Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
  • B. 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.
  • C. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • D. Nyköping
    Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
  • E. Norrköping
    Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
  • 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: Växjö
Triple: [Lübeck, twinCity, Växjö]
Generated description
Växjö is a city in southern Sweden known for its lakeside setting, environmental sustainability initiatives, and role as a regional cultural and educational center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Växjö
Target entity description: Växjö is a city in southern Sweden known for its lakeside setting, environmental sustainability initiatives, and role as a regional cultural and educational center.
  • A. Skövde
    Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
  • B. 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.
  • C. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • D. Nyköping
    Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
  • E. Norrköping
    Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
  • 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_69ab4a4ced288190ab6d3e062d10f7f6 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abe007329c8190b0bc1851c7307124 completed March 7, 2026, 8:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69b20f2e10b0819090455b876abe821b completed March 12, 2026, 12:56 a.m.
NEDg Description generation batch_69b20fb052a48190a3bf1a033752f9f4 completed March 12, 2026, 12:58 a.m.
NED2 Entity disambiguation (via description) batch_69b210087190819096e5dc919af46e09 completed March 12, 2026, 12:59 a.m.
Created at: March 6, 2026, 10:03 p.m.