Triple

T968718
Position Surface form Disambiguated ID Type / Status
Subject The Robber Bride E20896 entity
Predicate hasCharacter P2308 FINISHED
Object Zenia E127040 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: Zenia | Statement: [The Robber Bride, hasCharacter, Zenia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zenia
Context triple: [The Robber Bride, hasCharacter, Zenia]
  • A. Zenia chosen
    Zenia is a central, enigmatic and manipulative figure in Margaret Atwood's novel "The Robber Bride," whose disruptive influence profoundly affects the lives of three other women.
  • B. Zella
    Zella is an activewear and athleisure clothing brand known for its performance-focused yet stylish designs, sold at Nordstrom.
  • C. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • D. Valeria
    Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
  • E. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • 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_69a493b33d2c81909c52c369d3ca8436 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b4481f508190adcf0a965a23862c completed March 1, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac537ba7c08190966fa4a29da90310 completed March 7, 2026, 4:34 p.m.
Created at: March 1, 2026, 7:40 p.m.