Triple

T3554455
Position Surface form Disambiguated ID Type / Status
Subject Christina E75185 entity
Predicate hasVariant P455 FINISHED
Object Kristina
Kristina is a feminine given name commonly used in various European countries, often considered a variant of Christina.
 E368674 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: Kristina | Statement: [Christina, hasVariant, Kristina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kristina
Context triple: [Christina, hasVariant, Kristina]
  • A. Kristina Lugn
    Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
  • B. Katarina Frostenson
    Katarina Frostenson is a Swedish poet, writer, and former member of the Swedish Academy known for her influential and experimental contributions to contemporary Swedish literature.
  • C. Hedvig
    Hedvig is a Scandinavian female given name, historically borne by several notable women in Swedish and broader Nordic royalty and nobility.
  • D. Kaarina
    Kaarina is a town and municipality in southwestern Finland, located near the city of Turku.
  • E. Katrin
    Katrin is a feminine given name, commonly used in various European countries, that is a variant of the name Catherine.
  • 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: Kristina
Triple: [Christina, hasVariant, Kristina]
Generated description
Kristina is a feminine given name commonly used in various European countries, often considered a variant of Christina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kristina
Target entity description: Kristina is a feminine given name commonly used in various European countries, often considered a variant of Christina.
  • A. Kristina Lugn
    Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
  • B. Katarina Frostenson
    Katarina Frostenson is a Swedish poet, writer, and former member of the Swedish Academy known for her influential and experimental contributions to contemporary Swedish literature.
  • C. Hedvig
    Hedvig is a Scandinavian female given name, historically borne by several notable women in Swedish and broader Nordic royalty and nobility.
  • D. Kaarina
    Kaarina is a town and municipality in southwestern Finland, located near the city of Turku.
  • E. Katrin
    Katrin is a feminine given name, commonly used in various European countries, that is a variant of the name Catherine.
  • 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_69ad85d33c6c819081d5ac1df13b5680 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc05549d88190acdebdd542ea1a67 completed March 8, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bb95bcc88190abf1ea2efc30faeb completed March 13, 2026, 7:24 a.m.
NEDg Description generation batch_69b3bc7bf10c81908a1f30cd63892c90 completed March 13, 2026, 7:27 a.m.
NED2 Entity disambiguation (via description) batch_69b3e727222881909f8f57890fca1746 completed March 13, 2026, 10:29 a.m.
Created at: March 8, 2026, 3:20 p.m.