Triple

T15837239
Position Surface form Disambiguated ID Type / Status
Subject Cynthia Ozick E384015 entity
Predicate notableWork P4 FINISHED
Object Rosa
"Rosa" is a novella by Cynthia Ozick that follows a Holocaust survivor grappling with trauma, memory, and identity in postwar America.
E1180407 NE FINISHED

How this triple was built (4 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: Rosa | Statement: [Cynthia Ozick, notableWork, Rosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rosa
Context triple: [Cynthia Ozick, notableWork, Rosa]
  • A. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • B. Rosa
    "Rosa" is a song by Belgian singer-songwriter Jacques Brel, known for its poetic lyrics and emotive, theatrical style characteristic of his chanson repertoire.
  • C. Rosa
    Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
  • D. Rosa
    Rosa is a celebrated poem by Nikki Giovanni that honors civil rights icon Rosa Parks and reflects on the broader struggle for racial justice.
  • E. Rosa
    Rosa is a feminine given name of Latin origin meaning "rose," used in many languages and cultures.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rosa
Triple: [Cynthia Ozick, notableWork, Rosa]
Generated description
"Rosa" is a novella by Cynthia Ozick that follows a Holocaust survivor grappling with trauma, memory, and identity in postwar America.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Rosa
Target entity description: "Rosa" is a novella by Cynthia Ozick that follows a Holocaust survivor grappling with trauma, memory, and identity in postwar America.
  • A. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • B. Rosa
    Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
  • C. Rosa
    Rosa is a celebrated poem by Nikki Giovanni that honors civil rights icon Rosa Parks and reflects on the broader struggle for racial justice.
  • D. Rosa
    "Rosa" is a song by Belgian singer-songwriter Jacques Brel, known for its poetic lyrics and emotive, theatrical style characteristic of his chanson repertoire.
  • E. Rosa
    Rosa is a feminine given name of Latin origin meaning "rose," used in many languages and cultures.
  • F. None of above. chosen

Provenance (5 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_69d86da34c888190976e06c4019d415a completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e142e1fcd48190bcb884f6c65db847 completed April 16, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa13a83448190adcad8bb84622e55 completed May 9, 2026, 9:03 p.m.
NEDg Description generation batch_69ffa417ee248190808b0fecfb58d705 completed May 9, 2026, 9:16 p.m.
NED2 Entity disambiguation (via description) batch_69ffa5372f248190827cdc4985fee1ef completed May 9, 2026, 9:20 p.m.
Created at: April 10, 2026, 4:49 a.m.