Triple

T9751532
Position Surface form Disambiguated ID Type / Status
Subject Dark E236452 entity
Predicate writer P1360 FINISHED
Object Jantje Friese E818076 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: Jantje Friese | Statement: [Dark, writer, Jantje Friese]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jantje Friese
Context triple: [Dark, writer, Jantje Friese]
  • A. Jantje Friese chosen
    Jantje Friese is a German screenwriter and producer best known for co-creating the acclaimed science fiction thriller series "Dark."
  • B. Saskia de Jonge
    Saskia de Jonge is a Dutch former competitive swimmer who specialized in freestyle events and represented the Netherlands in international competitions, including the Olympic Games.
  • C. Simone Buitendijk
    Simone Buitendijk is a Dutch academic leader and scholar in higher education policy who has served as vice-chancellor of the University of Leeds.
  • D. Saskia Boddeke
    Saskia Boddeke is a Dutch multimedia artist and director known for her immersive installations, opera stagings, and frequent collaborations with filmmaker Peter Greenaway.
  • E. Iris Steensma
    Iris Steensma is the troubled teenage prostitute character from Martin Scorsese’s 1976 film "Taxi Driver," whose role became iconic through Jodie Foster’s acclaimed performance.
  • 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9facd5b881909f0569b23f308815 completed April 1, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1bcd60e1c81908ea2e38ca91e58f6 completed April 5, 2026, 1:37 a.m.
Created at: March 30, 2026, 8:24 p.m.