Triple

T12901598
Position Surface form Disambiguated ID Type / Status
Subject Seven of Nine E308623 entity
Predicate childhoodName P57918 FINISHED
Object Annika Hansen E1008177 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: Annika Hansen | Statement: [Seven of Nine, childhoodName, Annika Hansen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Annika Hansen
Context triple: [Seven of Nine, childhoodName, Annika Hansen]
  • A. Annika Hansen chosen
    Annika Hansen is the human birth name of Seven of Nine, a former Borg drone and prominent character in the Star Trek franchise.
  • B. Annika
    Annika is a television crime drama series featuring Kate Dickie in a prominent role.
  • C. Annika Persson
    Annika Persson is a notable individual who shares the Persson surname, which is common in Sweden and associated with several prominent figures in politics, business, and culture.
  • D. Annika Lammers
    Annika Lammers is a person notable enough to be recognized as a bearer of the surname Lammers.
  • E. Emma Grede
    Emma Grede is a British entrepreneur and fashion executive best known as the co-founder and CEO of Good American and a founding partner of Kim Kardashian’s shapewear brand SKIMS.
  • 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_69d7bdf7c1f0819098102569a8d8cbf5 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97180ee708190b60a3e58c42f764f completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af59f3cc81908c99bcde43e724e6 completed May 3, 2026, 2:13 a.m.
Created at: April 9, 2026, 5:40 p.m.