Triple

T2207026
Position Surface form Disambiguated ID Type / Status
Subject Sarpsborg E50822 entity
Predicate hasTwinTown P919 FINISHED
Object Norrköping
Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
 E289301 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: Norrköping | Statement: [Sarpsborg, hasTwinTown, Norrköping]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Norrköping
Context triple: [Sarpsborg, hasTwinTown, Norrköping]
  • A. 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.
  • 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. Enköping
    Enköping is a small Swedish town known for its numerous themed parks and gardens, often called “Sweden’s nearest town” due to its central location relative to several major cities.
  • D. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • E. Skövde
    Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
  • 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: Norrköping
Triple: [Sarpsborg, hasTwinTown, Norrköping]
Generated description
Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Norrköping
Target entity description: Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
  • A. 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.
  • 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. Enköping
    Enköping is a small Swedish town known for its numerous themed parks and gardens, often called “Sweden’s nearest town” due to its central location relative to several major cities.
  • D. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • E. Skövde
    Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
  • 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_69a88b06709c8190978fb2418470d1b6 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abbfcbb83081908d5b2f1603c7b4d2 completed March 7, 2026, 6:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf266ed881909206c87c69a2ea95 completed March 10, 2026, 5:41 a.m.
NEDg Description generation batch_69afafa8012481909c0b4d07c8803bf7 completed March 10, 2026, 5:44 a.m.
NED2 Entity disambiguation (via description) batch_69afb02231588190a2c6dbaf22c15b14 completed March 10, 2026, 5:46 a.m.
Created at: March 4, 2026, 7:46 p.m.