Triple

T6869876
Position Surface form Disambiguated ID Type / Status
Subject Richard Fleischer E158512 entity
Predicate notableWork P4 FINISHED
Object Red Sonja E333501 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: Red Sonja | Statement: [Richard Fleischer, notableWork, Red Sonja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Red Sonja
Context triple: [Richard Fleischer, notableWork, Red Sonja]
  • A. Red Sonja chosen
    Red Sonja is a sword-and-sorcery comic book heroine known for her fiery red hair, chainmail bikini armor, and fierce warrior persona in the Conan the Barbarian universe.
  • B. Conan the Barbarian
    Conan the Barbarian is a 1982 fantasy adventure film that helped launch Arnold Schwarzenegger to stardom through his portrayal of the iconic sword-and-sorcery hero.
  • C. Witchblade
    Witchblade is a supernatural comic book series, later adapted into television, centered on a mystical, sentient gauntlet that grants its female wielder powerful abilities.
  • D. The Sorceress
    The Sorceress is the main antagonist of Spyro: Year of the Dragon, a powerful magic-wielding villain who seeks to steal dragon eggs for her own dark purposes.
  • E. The Blood Lady
    The Blood Lady is a film featuring Russian actress Svetlana Khodchenkova in a prominent role, likely within the thriller or horror genre.
  • 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_69c68831e3648190a643c328122e4d43 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d8a916a88190b81551731dff2898 completed March 27, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c742a114008190be431f1e10d94501 completed March 28, 2026, 2:53 a.m.
Created at: March 27, 2026, 2:22 p.m.