Triple

T16766471
Position Surface form Disambiguated ID Type / Status
Subject Nina’s Tragedies E407476 entity
Predicate characterTrait P662 FINISHED
Object Nina is grieving E865433 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: Nina is grieving | Statement: [Nina’s Tragedies, characterTrait, Nina is grieving]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nina is grieving
Context triple: [Nina’s Tragedies, characterTrait, Nina is grieving]
  • A. Nina – Juliet Stevenson chosen
    Nina is the emotionally devastated yet resilient protagonist of the British romantic fantasy film "Truly, Madly, Deeply," whose journey through grief and love anchors the story.
  • B. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • 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. Nina
    Nina is a biographical drama film written and directed by Cynthia Mort that portrays the life and struggles of legendary musician Nina Simone.
  • E. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • 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_69d8839174188190909f190097207065 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b0330d6081908ce99f14c70b90f2 completed April 18, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00a531ea7c81908630f16f6c685d49 completed May 10, 2026, 3:33 p.m.
Created at: April 10, 2026, 5:21 a.m.