Triple

T8440813
Position Surface form Disambiguated ID Type / Status
Subject Nina Sosanya E199345 entity
Predicate notableWork P4 FINISHED
Object Vera E328915 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: Vera | Statement: [Nina Sosanya, notableWork, Vera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vera
Context triple: [Nina Sosanya, notableWork, Vera]
  • A. Vera
    Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
  • B. Vera chosen
    Vera is a feminine given name of Slavic origin, commonly used in Russian and other Eastern European cultures, meaning "faith."
  • C. Vera
    Vera is a memorable supporting character from the 1989 Eddie Murphy film "Harlem Nights," known for her tough, comedic persona.
  • D. Veronika
    Veronika is the troubled young protagonist of Paulo Coelho's novel "Veronika Decides to Die," whose suicide attempt leads her to a transformative stay in a mental institution.
  • E. Vera Bythiner
    Vera Bythiner was the wife of German-Jewish dentist Fritz Pfeffer, who is known for hiding with Anne Frank during World War II.
  • 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_69ca8314cd6c8190a6b8c2a1096e18f3 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe138a94081908e306d22aaa39b24 completed March 31, 2026, 2:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce1d9ab3a88190ada7741cf054fc1b completed April 2, 2026, 7:41 a.m.
Created at: March 30, 2026, 6:08 p.m.