Triple

T13437678
Position Surface form Disambiguated ID Type / Status
Subject Margaret Isabella Balfour E320271 entity
Predicate givenName P17 FINISHED
Object Isabella
Isabella is a feminine given name of Hebrew origin, commonly used in many European languages as a variant of Elizabeth.
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: [Margaret Isabella Balfour, givenName, Isabella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabella
Context triple: [Margaret Isabella Balfour, 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 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.
  • 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
  • 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: [Margaret Isabella Balfour, givenName, Isabella]
Generated description
Isabella is a feminine given name of Hebrew origin, commonly used in many European languages as a variant of Elizabeth.
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 used in many European languages as a variant of Elizabeth.
  • 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 the given name of Isabella II of Jerusalem, a 13th-century queen regnant of the Crusader Kingdom of Jerusalem.
  • 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 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.
  • E. Isabella
    Isabella was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
  • 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_69d80761e6cc8190a90c844589998ecc completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaee42a8c8190a85716b4a6db335e completed April 12, 2026, 2:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7399215bc8190b846906b0f081e7f completed May 3, 2026, 12:03 p.m.
NEDg Description generation batch_69f73bd79b8c8190bf91865e22bd41d0 completed May 3, 2026, 12:13 p.m.
NED2 Entity disambiguation (via description) batch_69f73c45b0748190ab9b1b524d206c0d completed May 3, 2026, 12:15 p.m.
Created at: April 9, 2026, 9:40 p.m.