Triple

T6080640
Position Surface form Disambiguated ID Type / Status
Subject Isabella Brant E135512 entity
Predicate hasGivenName P17 FINISHED
Object Isabella
Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used across many cultures.
E569457 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: Isabella | Statement: [Isabella Brant, hasGivenName, Isabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabella
Context triple: [Isabella Brant, hasGivenName, Isabella]
  • A. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • B. Isabella
    Isabella was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
  • C. Isabella
    Isabella was an English princess of the 13th century, daughter of King John of England, who became Lady de Coucy through marriage into the French nobility.
  • D. Isabella
    Isabella is the given name of Mrs Beeton, the famed 19th-century English author of the influential household management guide "Mrs Beeton's Book of Household Management."
  • E. Isabella
    Isabella is a devout and principled novice nun in Shakespeare's play "Measure for Measure," whose moral integrity is tested by corrupt authority.
  • 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: Isabella
Triple: [Isabella Brant, hasGivenName, Isabella]
Generated description
Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used across many cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Isabella
Target entity description: Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used across many cultures.
  • A. Isabella
    Isabella was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
  • B. Isabella
    Isabella is the given name of Mrs Beeton, the famed 19th-century English author of the influential household management guide "Mrs Beeton's Book of Household Management."
  • C. Isabella
    Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
  • D. Isabella
    Isabella was an English princess of the 13th century, daughter of King John of England, who became Lady de Coucy through marriage into the French nobility.
  • E. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • 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_69c0087ad31c8190ab936e0ff28614b6 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c057735b6081908b82757505fa7d5d completed March 22, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1252a178c81909a3d689ad748fb5e completed March 23, 2026, 11:34 a.m.
NEDg Description generation batch_69c1288420dc8190bd70eba4c1a789df completed March 23, 2026, 11:48 a.m.
NED2 Entity disambiguation (via description) batch_69c129259d988190aa53f1637ff05be5 completed March 23, 2026, 11:51 a.m.
Created at: March 22, 2026, 4:11 p.m.