Triple

T12256126
Position Surface form Disambiguated ID Type / Status
Subject Olof Lagercrantz E292104 entity
Predicate spouse P13 FINISHED
Object Martina Lagercrantz
Martina Lagercrantz was the wife of prominent Swedish literary critic and author Olof Lagercrantz.
 E973079 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: Martina Lagercrantz | Statement: [Olof Lagercrantz, spouse, Martina Lagercrantz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martina Lagercrantz
Context triple: [Olof Lagercrantz, spouse, Martina Lagercrantz]
  • A. Karin Larsson
    Karin Larsson was a Swedish artist and designer whose innovative interior and textile designs, created together with her husband Carl Larsson, became iconic for the Scandinavian Arts and Crafts style.
  • B. Åsa Larsson
    Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
  • C. Maud Runnström
    Maud Runnström was the wife of Swedish physicist and Nobel laureate Kai Siegbahn.
  • D. Stina Lindgren
    Stina Lindgren is known as the spouse of acclaimed Swedish author Torgny Lindgren.
  • E. Marianne Dahlbäck
    Marianne Dahlbäck is a Swedish architect best known for co-designing Stockholm’s Vasa Museum, one of Scandinavia’s most visited cultural landmarks.
  • 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: Martina Lagercrantz
Triple: [Olof Lagercrantz, spouse, Martina Lagercrantz]
Generated description
Martina Lagercrantz was the wife of prominent Swedish literary critic and author Olof Lagercrantz.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martina Lagercrantz
Target entity description: Martina Lagercrantz was the wife of prominent Swedish literary critic and author Olof Lagercrantz.
  • A. Karin Larsson
    Karin Larsson was a Swedish artist and designer whose innovative interior and textile designs, created together with her husband Carl Larsson, became iconic for the Scandinavian Arts and Crafts style.
  • B. Åsa Larsson
    Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
  • C. Maud Runnström
    Maud Runnström was the wife of Swedish physicist and Nobel laureate Kai Siegbahn.
  • D. Stina Lindgren
    Stina Lindgren is known as the spouse of acclaimed Swedish author Torgny Lindgren.
  • E. Marianne Dahlbäck
    Marianne Dahlbäck is a Swedish architect best known for co-designing Stockholm’s Vasa Museum, one of Scandinavia’s most visited cultural landmarks.
  • 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_69d6ab67950c8190be08450a06228c4b completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91cc9dd5081908880061d52351850 completed April 10, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e63da6081908840b1e37fd39b88 completed May 2, 2026, 3:55 p.m.
NEDg Description generation batch_69f61f5bc1fc8190af9d74acc307ebe1 completed May 2, 2026, 3:59 p.m.
NED2 Entity disambiguation (via description) batch_69f62041f2408190ad320fec5283abdd completed May 2, 2026, 4:03 p.m.
Created at: April 8, 2026, 9:52 p.m.