Triple

T3145536
Position Surface form Disambiguated ID Type / Status
Subject Västra Götaland County E65754 entity
Predicate contains P35 FINISHED
Object Alingsås
Alingsås is a Swedish town known for its historic wooden architecture, café culture, and annual Lights in Alingsås illumination festival.
 E363745 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: Alingsås | Statement: [Västra Götaland County, contains, Alingsås]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alingsås
Context triple: [Västra Götaland County, contains, Alingsås]
  • A. Eskilstuna
    Eskilstuna is an industrial city in central Sweden known historically for its metalworking and engineering industries.
  • B. 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.
  • C. Strängnäs
    Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
  • 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: Alingsås
Triple: [Västra Götaland County, contains, Alingsås]
Generated description
Alingsås is a Swedish town known for its historic wooden architecture, café culture, and annual Lights in Alingsås illumination festival.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alingsås
Target entity description: Alingsås is a Swedish town known for its historic wooden architecture, café culture, and annual Lights in Alingsås illumination festival.
  • A. Eskilstuna
    Eskilstuna is an industrial city in central Sweden known historically for its metalworking and engineering industries.
  • B. 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.
  • C. Strängnäs
    Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
  • 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_69ad8582f564819088c27e1f96153938 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada59797788190a8d71262888c5df0 completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373888b948190b84d7bfa908f15ad completed March 13, 2026, 2:16 a.m.
NEDg Description generation batch_69b3776ecca481908885e3c948b3a9f1 completed March 13, 2026, 2:33 a.m.
NED2 Entity disambiguation (via description) batch_69b377d036988190a8a17fbffe298844 completed March 13, 2026, 2:34 a.m.
Created at: March 8, 2026, 3:05 p.m.