Triple

T2289820
Position Surface form Disambiguated ID Type / Status
Subject Burlesque E51475 entity
Predicate character P662 FINISHED
Object Tess
Tess is a central character in the musical film "Burlesque," serving as the tough but caring owner and manager of the struggling burlesque club.
 E252772 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: Tess | Statement: [Burlesque, character, Tess]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tess
Context triple: [Burlesque, character, Tess]
  • A. The Farmer’s Daughter
    The Farmer’s Daughter is a 1947 American romantic comedy film starring Loretta Young as a Swedish-American farm girl who becomes involved in politics.
  • B. Adam Bede
    Adam Bede is a 1859 realist novel by George Eliot that portrays rural English life and moral dilemmas through the story of a principled carpenter and those around him.
  • C. Bathsheba
    Bathsheba is a prominent biblical figure known as the wife of King David and the mother of King Solomon.
  • D. Silas Marner
    Silas Marner is a novel by George Eliot that tells the story of a reclusive weaver whose life is transformed by the arrival of an orphaned child.
  • E. Nightingale
    Nightingale is the surname of Florence Nightingale, the pioneering 19th-century nurse and social reformer widely regarded as the founder of modern nursing.
  • 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: Tess
Triple: [Burlesque, character, Tess]
Generated description
Tess is a central character in the musical film "Burlesque," serving as the tough but caring owner and manager of the struggling burlesque club.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tess
Target entity description: Tess is a central character in the musical film "Burlesque," serving as the tough but caring owner and manager of the struggling burlesque club.
  • A. The Farmer’s Daughter
    The Farmer’s Daughter is a 1947 American romantic comedy film starring Loretta Young as a Swedish-American farm girl who becomes involved in politics.
  • B. Adam Bede
    Adam Bede is a 1859 realist novel by George Eliot that portrays rural English life and moral dilemmas through the story of a principled carpenter and those around him.
  • C. Bathsheba
    Bathsheba is a prominent biblical figure known as the wife of King David and the mother of King Solomon.
  • D. Silas Marner
    Silas Marner is a novel by George Eliot that tells the story of a reclusive weaver whose life is transformed by the arrival of an orphaned child.
  • E. Nightingale
    Nightingale is the surname of Florence Nightingale, the pioneering 19th-century nurse and social reformer widely regarded as the founder of modern nursing.
  • 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_69a88b09c644819090b503456d96bf70 completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc273b67c8190bcd96f9a484647ef completed March 7, 2026, 6:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7f1e84ac819096cb62ce5e94d865 completed March 9, 2026, 8:04 a.m.
NEDg Description generation batch_69ae7fee12ac8190bb9924f7467434a6 completed March 9, 2026, 8:08 a.m.
NED2 Entity disambiguation (via description) batch_69ae8061cd348190b0b0b65dcf730f99 completed March 9, 2026, 8:10 a.m.
Created at: March 4, 2026, 7:48 p.m.