Triple

T4225480
Position Surface form Disambiguated ID Type / Status
Subject Clara E94446 entity
Predicate relatedName P3889 FINISHED
Object Clarissa E350508 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: Clarissa | Statement: [Clara, relatedName, Clarissa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Clarissa
Context triple: [Clara, relatedName, Clarissa]
  • A. Clarissa chosen
    Clarissa is the given first name of Clara Barton, the pioneering American nurse and founder of the American Red Cross.
  • B. Pamela
    Pamela is the given name of Pam Grier, the pioneering American actress celebrated for her iconic roles in 1970s blaxploitation films and later works like "Jackie Brown."
  • C. Marianne
    Marianne was a 19th-century Dutch princess of the House of Orange-Nassau, known for her independent spirit, unconventional personal life, and extensive patronage of the arts and architecture.
  • D. Marianne
    Marianne is the national personification of the French Republic, symbolizing liberty, reason, and the values of the nation.
  • E. Fanny Eden
    Fanny Eden was a member of the British Eden family in colonial India, after whom the famous cricket ground Eden Gardens in Kolkata was named.
  • 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_69b3453700a08190ae88792e3dc63207 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e4d32d481909df7b18f502945b8 completed March 12, 2026, 11:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69b59647bd948190afc168ba1057a52f completed March 14, 2026, 5:09 p.m.
Created at: March 12, 2026, 11:04 p.m.