Triple

T3314023
Position Surface form Disambiguated ID Type / Status
Subject Game of Thrones season 4 E69636 entity
Predicate mainCastMember P5563 FINISHED
Object Carice van Houten E226238 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: Carice van Houten | Statement: [Game of Thrones season 4, mainCastMember, Carice van Houten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carice van Houten
Context triple: [Game of Thrones season 4, mainCastMember, Carice van Houten]
  • A. Carice van Houten chosen
    Carice van Houten is a Dutch actress best known internationally for her role as Melisandre in the television series "Game of Thrones."
  • B. Annette Kurschus
    Annette Kurschus is a German Protestant theologian and bishop who has served as a leading figure in the Evangelical Church in Germany.
  • C. Beth Riesgraf
    Beth Riesgraf is an American actress best known for playing the quirky thief Parker on the television series "Leverage."
  • D. Anneke Wills
    Anneke Wills is a British actress best known for playing the companion Polly in the classic science fiction television series Doctor Who during the 1960s.
  • E. Franka Potente
    Franka Potente is a German actress best known internationally for her breakout role in "Run Lola Run" and her appearances in the Bourne film series.
  • 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_69ad85a0bb048190a5458d2738012d61 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb10f97b48190afb9c3864faf8cb2 completed March 8, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3f760348190abd8854c369cb41b completed March 12, 2026, 5:12 p.m.
Created at: March 8, 2026, 3:11 p.m.