Triple

T11565344
Position Surface form Disambiguated ID Type / Status
Subject Francis Fukuyama E274236 entity
Predicate spouse P13 FINISHED
Object Laura Holmgren
Laura Holmgren is the wife of American political scientist and author Francis Fukuyama.
E935336 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: Laura Holmgren | Statement: [Francis Fukuyama, spouse, Laura Holmgren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Holmgren
Context triple: [Francis Fukuyama, spouse, Laura Holmgren]
  • A. Anna Wahlgren
    Anna Wahlgren was a Swedish author and influential parenting advisor best known for her controversial childcare books and public role in debates on child-rearing.
  • B. Inga Swenson
    Inga Swenson was an American actress known for her acclaimed stage and screen performances, including notable roles on Broadway and in film and television.
  • C. Karin Hansson
    Karin Hansson is known primarily as the daughter of Swedish Prime Minister Per Albin Hansson.
  • D. Ellen Lundström
    Ellen Lundström was the first wife of renowned Swedish film director Ingmar Bergman, with whom he had several children before their divorce.
  • E. Sari Lindblom
    Sari Lindblom is a Finnish academic and higher education leader who serves as the rector of the University of Helsinki.
  • 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: Laura Holmgren
Triple: [Francis Fukuyama, spouse, Laura Holmgren]
Generated description
Laura Holmgren is the wife of American political scientist and author Francis Fukuyama.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Laura Holmgren
Target entity description: Laura Holmgren is the wife of American political scientist and author Francis Fukuyama.
  • A. Anna Wahlgren
    Anna Wahlgren was a Swedish author and influential parenting advisor best known for her controversial childcare books and public role in debates on child-rearing.
  • B. Inga Swenson
    Inga Swenson was an American actress known for her acclaimed stage and screen performances, including notable roles on Broadway and in film and television.
  • C. Karin Hansson
    Karin Hansson is known primarily as the daughter of Swedish Prime Minister Per Albin Hansson.
  • D. Ellen Lundström
    Ellen Lundström was the first wife of renowned Swedish film director Ingmar Bergman, with whom he had several children before their divorce.
  • E. Sari Lindblom
    Sari Lindblom is a Finnish academic and higher education leader who serves as the rector of the University of Helsinki.
  • 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_69d6aae5ac3c81908d2b0a3a665665b2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d88dd321f88190a57ecaf079fbbc3f completed April 10, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69e713bcc0048190bec14ac4ab84d51d completed April 21, 2026, 6:05 a.m.
NEDg Description generation batch_69e720f4015c81909ba7973c3e781985 completed April 21, 2026, 7:02 a.m.
NED2 Entity disambiguation (via description) batch_69e75a7a04c88190bb8f3dd3f3e435ef completed April 21, 2026, 11:07 a.m.
Created at: April 8, 2026, 9:37 p.m.