Triple

T1686438
Position Surface form Disambiguated ID Type / Status
Subject Mikhail E36451 entity
Predicate equivalentName P6530 FINISHED
Object Mikael E99056 NE FINISHED

How this triple was built (2 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: Mikael | Statement: [Mikhail, equivalentName, Mikael]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mikael
Context triple: [Mikhail, equivalentName, Mikael]
  • A. Mikael chosen
    Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
  • 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. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • D. Mikael Olavinpoika
    Mikael Olavinpoika, better known as Mikael Agricola, was a 16th-century Finnish clergyman and scholar regarded as the father of written Finnish and a key figure in the Protestant Reformation in Finland.
  • E. Sven
    Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a886151508819084fa7f1ce6e05577 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6293c368819094ab0f615e418647 completed March 6, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad71c344fc8190926db828cf09550e completed March 8, 2026, 12:55 p.m.
Created at: March 4, 2026, 7:29 p.m.