Triple

T2748470
Position Surface form Disambiguated ID Type / Status
Subject The Most Happy Fella E60925 entity
Predicate notableCharacter P1481 FINISHED
Object Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
 E301310 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: Rosabella | Statement: [The Most Happy Fella, notableCharacter, Rosabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosabella
Context triple: [The Most Happy Fella, notableCharacter, Rosabella]
  • A. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • B. Graziella
    Graziella is a feminine given name of Italian origin, often associated with grace and elegance.
  • C. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Leonora
    Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
  • 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: Rosabella
Triple: [The Most Happy Fella, notableCharacter, Rosabella]
Generated description
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosabella
Target entity description: Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • A. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • B. Graziella
    Graziella is a feminine given name of Italian origin, often associated with grace and elegance.
  • C. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Leonora
    Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
  • 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_69ab4b79846081909096725374d65ce9 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb517a00819084fd8f8933a25212 completed March 7, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69afce87cb3c8190a9cb28a443b787e0 completed March 10, 2026, 7:55 a.m.
NEDg Description generation batch_69afcfb39a808190a238df2b0c958ee6 completed March 10, 2026, 8 a.m.
NED2 Entity disambiguation (via description) batch_69afd00cff448190b9b580f972494d8c completed March 10, 2026, 8:02 a.m.
Created at: March 6, 2026, 9:56 p.m.