Triple

T1061230
Position Surface form Disambiguated ID Type / Status
Subject The Witty Fair One E22910 entity
Predicate hasCharacter P2308 FINISHED
Object Valeria
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
 E128082 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: Valeria | Statement: [The Witty Fair One, hasCharacter, Valeria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Valeria
Context triple: [The Witty Fair One, hasCharacter, Valeria]
  • A. Valeria
    Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • D. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • E. Valerie
    "Valerie" is a 1957 American Western film starring Sterling Hayden, loosely inspired by the Rashomon-style multiple-perspective narrative.
  • 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: Valeria
Triple: [The Witty Fair One, hasCharacter, Valeria]
Generated description
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Valeria
Target entity description: Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
  • A. Valeria
    Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • D. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • E. Valerie
    "Valerie" is a 1957 American Western film starring Sterling Hayden, loosely inspired by the Rashomon-style multiple-perspective narrative.
  • 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_69a493dada0481909c43649f9843ea91 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b8f531f481909a40558811379992 completed March 1, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac538856b481908d6a25ee05fb4885 completed March 7, 2026, 4:34 p.m.
NEDg Description generation batch_69ac544fc41881908daff6b313622619 completed March 7, 2026, 4:37 p.m.
NED2 Entity disambiguation (via description) batch_69ac5526679081909c9f7458bd316ff6 completed March 7, 2026, 4:41 p.m.
Created at: March 1, 2026, 7:42 p.m.