Triple

T15129792
Position Surface form Disambiguated ID Type / Status
Subject Isabella Beecher Hooker E361387 entity
Predicate givenName P17 FINISHED
Object Isabella
Isabella is a feminine given name of Hebrew origin, commonly 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 Beecher Hooker, givenName, Isabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabella
Context triple: [Isabella Beecher Hooker, 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 was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
  • 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 of Portugal was a 16th-century Portuguese noblewoman who became Holy Roman Empress and Queen of Spain as the wife of Emperor Charles V.
  • E. Isabella
    Isabella was a Polish princess of the Jagiellonian dynasty who became Queen consort of Hungary in the 16th century.
  • 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 Beecher Hooker, givenName, Isabella]
Generated description
Isabella is a feminine given name of Hebrew origin, commonly 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 Hebrew origin, commonly 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 the given name of Isabella II of Jerusalem, a 13th-century queen regnant of the Crusader Kingdom of Jerusalem.
  • 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 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_69d85a06450081909c5a14ea9851a15e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e005aff2648190bda885c09421758d completed April 15, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69febfe8802c81909af9dd11a7805f31 completed May 9, 2026, 5:02 a.m.
NEDg Description generation batch_69fec0c97f4c819084ba9eb2d8f69ceb completed May 9, 2026, 5:06 a.m.
NED2 Entity disambiguation (via description) batch_69fec13ccea48190aeb155af012478b8 completed May 9, 2026, 5:08 a.m.
Created at: April 10, 2026, 3:06 a.m.