Triple

T9832430
Position Surface form Disambiguated ID Type / Status
Subject Maria Schneider E239017 entity
Predicate notableWork P4 FINISHED
Object Allegresse
Allegresse is a celebrated jazz composition and album by Maria Schneider, showcasing her richly orchestrated, lyrical big band writing.
 E824375 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: Allegresse | Statement: [Maria Schneider, notableWork, Allegresse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Allegresse
Context triple: [Maria Schneider, notableWork, Allegresse]
  • A. Mignon
    Mignon is a mysterious, ethereal child of Italian origin who becomes one of the most poignant and symbolically rich figures in Goethe’s novel "Wilhelm Meister’s Apprenticeship."
  • B. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • C. Axelina
    Axelina is a feminine given name of Scandinavian origin, used as one of the personal names of Carin Axelina Hulda Fock.
  • D. Amorina
    Amorina is a 19th-century Swedish novel by Carl Jonas Love Almqvist, known for its romantic and psychological depth within early modern Swedish literature.
  • E. Sybille
    Sybille was a French frigate that took part in the early 19th-century naval engagement known as the Battle of San Domingo.
  • 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: Allegresse
Triple: [Maria Schneider, notableWork, Allegresse]
Generated description
Allegresse is a celebrated jazz composition and album by Maria Schneider, showcasing her richly orchestrated, lyrical big band writing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Allegresse
Target entity description: Allegresse is a celebrated jazz composition and album by Maria Schneider, showcasing her richly orchestrated, lyrical big band writing.
  • A. Mignon
    Mignon is a mysterious, ethereal child of Italian origin who becomes one of the most poignant and symbolically rich figures in Goethe’s novel "Wilhelm Meister’s Apprenticeship."
  • B. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • C. Axelina
    Axelina is a feminine given name of Scandinavian origin, used as one of the personal names of Carin Axelina Hulda Fock.
  • D. Amorina
    Amorina is a 19th-century Swedish novel by Carl Jonas Love Almqvist, known for its romantic and psychological depth within early modern Swedish literature.
  • E. Sybille
    Sybille was a French frigate that took part in the early 19th-century naval engagement known as the Battle of San Domingo.
  • 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_69ca84e314108190978324a4bdb959f8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb335623c8190902de29795bce87d completed April 2, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1d5c448388190818e4cc5e3a42dfc completed April 5, 2026, 3:23 a.m.
NEDg Description generation batch_69d1d6affc3c8190839a4db8f4271309 completed April 5, 2026, 3:27 a.m.
NED2 Entity disambiguation (via description) batch_69d1d772de00819089eed8be9f5ce3ce completed April 5, 2026, 3:30 a.m.
Created at: March 30, 2026, 8:32 p.m.