Triple

T21572074
Position Surface form Disambiguated ID Type / Status
Subject Isabella Gethin Shawe E532307 entity
Predicate givenName P17 FINISHED
Object Isabella
Isabella is a feminine given name of Spanish and Italian origin, widely used in many cultures and often associated with historical queens and literary characters.
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 Gethin Shawe, givenName, Isabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabella
Context triple: [Isabella Gethin Shawe, givenName, Isabella]
  • A. Isabella
    Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
  • B. Isabella
    Isabella was a 15th-century noblewoman who held the title of Duchess of Coimbra in the Kingdom of Portugal.
  • C. Isabella
    Isabella was a 15th-century Portuguese infanta who became Queen of Castile through her marriage to King John II.
  • D. Isabella
    Isabella is a fictional character portrayed by American actress Lexi Underwood.
  • E. Isabella
    Isabella was a powerful late 15th-century queen of Castile who, alongside her husband Ferdinand II of Aragon, completed the Reconquista and sponsored Christopher Columbus’s voyage that led to the European discovery of the Americas.
  • 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 Gethin Shawe, givenName, Isabella]
Generated description
Isabella is a feminine given name of Spanish and Italian origin, widely used in many cultures and often associated with historical queens and literary characters.
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, widely used in many cultures and often associated with historical queens and literary characters.
  • A. Isabella chosen
    Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used across many cultures.
  • B. Isabella
    Isabella is the given name of Lady Gregory, the influential Irish dramatist, folklorist, and co-founder of Dublin’s Abbey Theatre.
  • C. Isabella
    Isabella was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
  • D. Isabella
    Isabella was a 15th-century Portuguese infanta who became Queen of Castile through her marriage to King John II.
  • E. Isabella
    Isabella was a medieval European queen consort, notably Isabella of France who became Queen of England as the wife of Edward II and played a key role in his overthrow.
  • F. None of above.

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_69e0c460db088190828c64206a450273 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eee9cd50188190b44eb25fb87312bb completed April 27, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09f672b5808190a2dd61c26ce64403 completed May 17, 2026, 5:10 p.m.
NEDg Description generation batch_6a09f78a3d788190b4b19f3b6463550e completed May 17, 2026, 5:14 p.m.
NED2 Entity disambiguation (via description) batch_6a09f825b02c819096f57b1dba02ded2 completed May 17, 2026, 5:17 p.m.
Created at: April 16, 2026, 6:30 p.m.