Triple

T5981177
Position Surface form Disambiguated ID Type / Status
Subject Sweden women's national football team E133120 entity
Predicate nickname P55 FINISHED
Object Blågult
Blågult is the popular Swedish nickname for the Sweden women's national football team, referencing the country's blue and yellow colors.
 E559464 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: Blågult | Statement: [Sweden women's national football team, nickname, Blågult]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Blågult
Context triple: [Sweden women's national football team, nickname, Blågult]
  • A. Blå
    Blå is a renowned live music and cultural venue in Oslo, Norway, known for its vibrant jazz, electronic, and alternative music scene.
  • B. Blau
    The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
  • C. Blåvitt
    Blåvitt is the popular nickname of IFK Göteborg, one of Sweden’s most successful and historically significant football clubs.
  • D. Geel
    Geel is a city in the Flemish region of Belgium, noted for its long-standing tradition of community-based psychiatric care.
  • E. Gelb
    Gelb is a surname most prominently associated with Peter Gelb, the influential general manager of the Metropolitan Opera in New York City.
  • 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: Blågult
Triple: [Sweden women's national football team, nickname, Blågult]
Generated description
Blågult is the popular Swedish nickname for the Sweden women's national football team, referencing the country's blue and yellow colors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Blågult
Target entity description: Blågult is the popular Swedish nickname for the Sweden women's national football team, referencing the country's blue and yellow colors.
  • A. Blå
    Blå is a renowned live music and cultural venue in Oslo, Norway, known for its vibrant jazz, electronic, and alternative music scene.
  • B. Blau
    The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
  • C. Blåvitt
    Blåvitt is the popular nickname of IFK Göteborg, one of Sweden’s most successful and historically significant football clubs.
  • D. Geel
    Geel is a city in the Flemish region of Belgium, noted for its long-standing tradition of community-based psychiatric care.
  • E. Gelb
    Gelb is a surname most prominently associated with Peter Gelb, the influential general manager of the Metropolitan Opera in New York City.
  • 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_69c0086f45e8819098f73dd16d45ec9d completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04a67c3248190ba35a7121eb49672 completed March 22, 2026, 8 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0e42174a88190b8b40cbc7815911f completed March 23, 2026, 6:56 a.m.
NEDg Description generation batch_69c0f602b79881909a6d971972f760b1 completed March 23, 2026, 8:12 a.m.
NED2 Entity disambiguation (via description) batch_69c0f6a75b908190b35d13b9593cf21f completed March 23, 2026, 8:15 a.m.
Created at: March 22, 2026, 4:04 p.m.