Triple

T16590282
Position Surface form Disambiguated ID Type / Status
Subject Isabella Sermon E403065 entity
Predicate givenName P17 FINISHED
Object Isabella
Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used in 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 Sermon, givenName, Isabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabella
Context triple: [Isabella Sermon, 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 Sermon, givenName, Isabella]
Generated description
Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used in 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 in many cultures.
  • 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 the given name of Isabella II of Jerusalem, a 13th-century queen regnant of the Crusader Kingdom of Jerusalem.
  • E. 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."
  • 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_69d88387363c8190a97a0c942130de97 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e359a012e081909a0604dde3c04bbb completed April 18, 2026, 10:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a007599dcd4819089bbd0569b3d9a12 completed May 10, 2026, 12:10 p.m.
NEDg Description generation batch_6a0079bf50dc8190a20057ef8a738e1d completed May 10, 2026, 12:27 p.m.
NED2 Entity disambiguation (via description) batch_6a007a34b42081908a77a3913c40377f completed May 10, 2026, 12:29 p.m.
Created at: April 10, 2026, 5:16 a.m.