Triple

T11704549
Position Surface form Disambiguated ID Type / Status
Subject Persson E278204 entity
Predicate hasNotableBearer P458 FINISHED
Object Annika Persson
Annika Persson is a notable individual who shares the Persson surname, which is common in Sweden and associated with several prominent figures in politics, business, and culture.
E943319 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: Annika Persson | Statement: [Persson, hasNotableBearer, Annika Persson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Annika Persson
Context triple: [Persson, hasNotableBearer, Annika Persson]
  • A. Ylva Johansson
    Ylva Johansson is a Swedish politician who has served as European Commissioner for Home Affairs and previously held several ministerial posts in the Swedish government.
  • B. Lisa Persdotter
    Lisa Persdotter is a Swedish businesswoman known for her marriage to software pioneer and space tourist Charles Simonyi.
  • C. Anna-Lisa Hansson
    Anna-Lisa Hansson was a daughter of Swedish Prime Minister Per Albin Hansson and a member of his prominent political family.
  • D. Greta Lundgren
    Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
  • E. Åsa Larsson
    Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
  • 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: Annika Persson
Triple: [Persson, hasNotableBearer, Annika Persson]
Generated description
Annika Persson is a notable individual who shares the Persson surname, which is common in Sweden and associated with several prominent figures in politics, business, and culture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Annika Persson
Target entity description: Annika Persson is a notable individual who shares the Persson surname, which is common in Sweden and associated with several prominent figures in politics, business, and culture.
  • A. Ylva Johansson
    Ylva Johansson is a Swedish politician who has served as European Commissioner for Home Affairs and previously held several ministerial posts in the Swedish government.
  • B. Lisa Persdotter
    Lisa Persdotter is a Swedish businesswoman known for her marriage to software pioneer and space tourist Charles Simonyi.
  • C. Anna-Lisa Hansson
    Anna-Lisa Hansson was a daughter of Swedish Prime Minister Per Albin Hansson and a member of his prominent political family.
  • D. Greta Lundgren
    Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
  • E. Åsa Larsson
    Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
  • 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a49c8c38819083d83f5fdec52b7f completed April 10, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69f0195739348190b40a378ca227cf85 completed April 28, 2026, 2:20 a.m.
NEDg Description generation batch_69f0319271788190a105828ae7582668 completed April 28, 2026, 4:03 a.m.
NED2 Entity disambiguation (via description) batch_69f05a44dcb88190a0bb57b0c8fef6b9 completed April 28, 2026, 6:57 a.m.
Created at: April 8, 2026, 9:40 p.m.