Triple

T7163156
Position Surface form Disambiguated ID Type / Status
Subject Mikkel S. Eriksen E166996 entity
Predicate givenName P17 FINISHED
Object Mikkel E330165 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: Mikkel | Statement: [Mikkel S. Eriksen, givenName, Mikkel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mikkel
Context triple: [Mikkel S. Eriksen, givenName, Mikkel]
  • A. Mads Dittmann Mikkelsen
    Mads Dittmann Mikkelsen is a Danish actor renowned for his versatile performances in films and television series such as "Casino Royale," "Hannibal," and "Another Round."
  • B. Mikael
    Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
  • C. Jørgen
    Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
  • D. Mikkel Eriksen chosen
    Mikkel Eriksen is a Norwegian record producer and songwriter best known as one half of the production duo Stargate, which has crafted numerous global pop and R&B hits.
  • E. Mikkel Svane
    Mikkel Svane is a Danish entrepreneur best known as the co-founder and longtime CEO of the customer service software company Zendesk.
  • 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_69c68888c10c819095e0383020225758 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e82feee481908fa180ea8c9924fa completed March 27, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7adc8b06c81909791e38becb594f6 completed March 28, 2026, 10:30 a.m.
Created at: March 27, 2026, 2:47 p.m.