Triple

T1830472
Position Surface form Disambiguated ID Type / Status
Subject Lauri Kristian Relander E40750 entity
Predicate givenName P17 FINISHED
Object Kristian
Kristian is a given name notably borne by Lauri Kristian Relander, the second President of Finland.
 E203472 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: Kristian | Statement: [Lauri Kristian Relander, givenName, Kristian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kristian
Context triple: [Lauri Kristian Relander, givenName, Kristian]
  • A. Kristian Welhaven
    Kristian Welhaven was a Norwegian police chief and public official, known for his prominent role in Oslo’s law enforcement in the early 20th century.
  • B. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • C. Søren
    Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
  • D. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • E. Henrik
    Henrik is the given name of the renowned Norwegian mathematician Niels Henrik Abel, known for his pioneering work in algebra and analysis.
  • 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: Kristian
Triple: [Lauri Kristian Relander, givenName, Kristian]
Generated description
Kristian is a given name notably borne by Lauri Kristian Relander, the second President of Finland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kristian
Target entity description: Kristian is a given name notably borne by Lauri Kristian Relander, the second President of Finland.
  • A. Kristian Welhaven
    Kristian Welhaven was a Norwegian police chief and public official, known for his prominent role in Oslo’s law enforcement in the early 20th century.
  • B. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • C. Søren
    Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
  • D. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • E. Henrik
    Henrik is the given name of the renowned Norwegian mathematician Niels Henrik Abel, known for his pioneering work in algebra and analysis.
  • 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_69a8864644bc8190b2358ab897194ac1 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0144cc08190abd1a6cf44e64daf completed March 7, 2026, 4:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69adbf6d49988190b8cb1773609a379b completed March 8, 2026, 6:26 p.m.
NEDg Description generation batch_69adc07fff60819092b10dd0e417ac5a completed March 8, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69adc0fd79c48190864f53a90517edc6 completed March 8, 2026, 6:33 p.m.
Created at: March 4, 2026, 7:32 p.m.