Triple

T1146348
Position Surface form Disambiguated ID Type / Status
Subject Morten Lie E23574 entity
Predicate hasGivenName P17 FINISHED
Object Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
E139012 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: Morten | Statement: [Morten Lie, hasGivenName, Morten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Morten
Context triple: [Morten Lie, hasGivenName, Morten]
  • A. 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.
  • B. Henrik
    Henrik is the given name of the renowned Norwegian mathematician Niels Henrik Abel, known for his pioneering work in algebra and analysis.
  • C. Mikael
    Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
  • D. Kristian Eidnes Andersen
    Kristian Eidnes Andersen is a Danish film composer and sound designer known for his atmospheric scores and collaborations with prominent European directors.
  • E. Tyge
    Tyge is the original Danish given name of the renowned 16th-century astronomer Tycho Brahe.
  • 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: Morten
Triple: [Morten Lie, hasGivenName, Morten]
Generated description
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Morten
Target entity description: Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • A. 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.
  • B. Henrik
    Henrik is the given name of the renowned Norwegian mathematician Niels Henrik Abel, known for his pioneering work in algebra and analysis.
  • C. Mikael
    Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
  • D. Kristian Eidnes Andersen
    Kristian Eidnes Andersen is a Danish film composer and sound designer known for his atmospheric scores and collaborations with prominent European directors.
  • E. Tyge
    Tyge is the original Danish given name of the renowned 16th-century astronomer Tycho Brahe.
  • 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_69a493ef399c8190b04b9146d2314f59 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc6e8c2081909fb3534413b7aacb completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac830d57e0819086fd19e032a589cd completed March 7, 2026, 7:57 p.m.
NEDg Description generation batch_69ac837e06cc8190b0da34646fa78c0c completed March 7, 2026, 7:58 p.m.
NED2 Entity disambiguation (via description) batch_69ac84309acc8190aac6c3c78246b352 completed March 7, 2026, 8:01 p.m.
Created at: March 1, 2026, 7:44 p.m.