Triple

T11145718
Position Surface form Disambiguated ID Type / Status
Subject Princess Zahra Aga Khan E263664 entity
Predicate givenName P17 FINISHED
Object Zahra
Zahra is the given name of Princess Zahra Aga Khan, a prominent member of the Aga Khan family known for her work in international development and philanthropy.
 E908202 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: Zahra | Statement: [Princess Zahra Aga Khan, givenName, Zahra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zahra
Context triple: [Princess Zahra Aga Khan, givenName, Zahra]
  • A. Zohra
    Zohra is a character in Naguib Mahfouz’s novel "Miramar," which centers on the lives and conflicts of residents in a pension in Alexandria, Egypt.
  • B. Maryam
    Maryam is a revered figure in Islam, honored in the Qur’an as the mother of Prophet Isa (Jesus) and a model of piety and devotion.
  • C. Roshanak
    Roshanak is an ancient Persian female given name, often associated with Roxana, the wife of Alexander the Great.
  • D. Fahdah
    Fahdah is a Saudi princess, formally known as Princess Fahdah Mohammed Abunayyan, associated with the Saudi royal family.
  • E. Unaizah
    Unaizah is a historic oasis city in central Saudi Arabia’s Qassim region, known for its date farms, traditional markets, and cultural heritage.
  • 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: Zahra
Triple: [Princess Zahra Aga Khan, givenName, Zahra]
Generated description
Zahra is the given name of Princess Zahra Aga Khan, a prominent member of the Aga Khan family known for her work in international development and philanthropy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zahra
Target entity description: Zahra is the given name of Princess Zahra Aga Khan, a prominent member of the Aga Khan family known for her work in international development and philanthropy.
  • A. Zohra
    Zohra is a character in Naguib Mahfouz’s novel "Miramar," which centers on the lives and conflicts of residents in a pension in Alexandria, Egypt.
  • B. Maryam
    Maryam is a revered figure in Islam, honored in the Qur’an as the mother of Prophet Isa (Jesus) and a model of piety and devotion.
  • C. Roshanak
    Roshanak is an ancient Persian female given name, often associated with Roxana, the wife of Alexander the Great.
  • D. Fahdah
    Fahdah is a Saudi princess, formally known as Princess Fahdah Mohammed Abunayyan, associated with the Saudi royal family.
  • E. Unaizah
    Unaizah is a historic oasis city in central Saudi Arabia’s Qassim region, known for its date farms, traditional markets, and cultural heritage.
  • F. None of above. chosen

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_69d6aa9ccddc8190868998c8b7beb060 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8634d5481909b114d30a542ea3f completed April 9, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69e46325e1308190af5718e10ffe1e8c completed April 19, 2026, 5:07 a.m.
NEDg Description generation batch_69e4666f98ac81908b3d3b8a6a8af8c9 completed April 19, 2026, 5:21 a.m.
NED2 Entity disambiguation (via description) batch_69e46c3f28dc8190a521c00151b01fde completed April 19, 2026, 5:46 a.m.
Created at: April 8, 2026, 9:28 p.m.