Triple

T16558759
Position Surface form Disambiguated ID Type / Status
Subject Isabella Ward E402280 entity
Predicate givenName P17 FINISHED
Object Isabella
Isabella is a feminine given name of Spanish and Italian origin, commonly used in many countries and often associated with royalty and historical figures.
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 Ward, givenName, Isabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabella
Context triple: [Isabella Ward, givenName, Isabella]
  • 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 Lady Gregory, the influential Irish dramatist, folklorist, and co-founder of Dublin’s Abbey Theatre.
  • C. Isabella
    Isabella of Portugal was a 16th-century Portuguese noblewoman who became Holy Roman Empress and Queen of Spain as the wife of Emperor Charles V.
  • D. Isabella
    Isabella was a Polish princess of the Jagiellonian dynasty who became Queen consort of Hungary in the 16th century.
  • E. Isabella
    Isabella was a 15th-century noblewoman who held the title of Duchess of Coimbra in the Kingdom of Portugal.
  • 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 Ward, givenName, Isabella]
Generated description
Isabella is a feminine given name of Spanish and Italian origin, commonly used in many countries and often associated with royalty and historical figures.
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, commonly used in many countries and often associated with royalty and historical figures.
  • 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 was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
  • C. Isabella
    Isabella is the given name of Lady Gregory, the influential Irish dramatist, folklorist, and co-founder of Dublin’s Abbey Theatre.
  • D. Isabella
    Isabella was a Polish princess of the Jagiellonian dynasty who became Queen consort of Hungary in the 16th century.
  • E. Isabella
    Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
  • 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_69d8838648088190acf97ef11fc3f61b completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3576bce0c819087ab36f7dec5c394 completed April 18, 2026, 10:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0067bcb698819092ede6ba4f8a4a2b completed May 10, 2026, 11:10 a.m.
NEDg Description generation batch_6a0068521c0c819093ddd51aa6f25995 completed May 10, 2026, 11:13 a.m.
NED2 Entity disambiguation (via description) batch_6a0068ab40d08190b8998c8f97bd34a5 completed May 10, 2026, 11:14 a.m.
Created at: April 10, 2026, 5:15 a.m.