Triple

T5831253
Position Surface form Disambiguated ID Type / Status
Subject Fawzia of Egypt E129352 entity
Predicate givenName P17 FINISHED
Object Fawzia
Fawzia is a feminine given name most famously borne by Princess Fawzia of Egypt, a 20th-century Egyptian royal and first wife of Iran’s Shah Mohammad Reza Pahlavi.
 E563323 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: Fawzia | Statement: [Fawzia of Egypt, givenName, Fawzia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fawzia
Context triple: [Fawzia of Egypt, givenName, Fawzia]
  • A. Habiba
    Habiba is a feminine given name commonly used in Arabic-speaking and Muslim-majority cultures, meaning "beloved" or "darling."
  • B. 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.
  • C. Rashida
    Rashida is a character featured in the film "Out of the Game."
  • D. Aziza
    Aziza is a traditional deity revered in Urhobo religion, associated with spiritual protection and guidance within the culture of the Urhobo people of Nigeria.
  • E. Khanum
    Khanum is a historical Turkic and Mongol honorific title used for noblewomen or female rulers, roughly equivalent to "queen" or "lady."
  • 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: Fawzia
Triple: [Fawzia of Egypt, givenName, Fawzia]
Generated description
Fawzia is a feminine given name most famously borne by Princess Fawzia of Egypt, a 20th-century Egyptian royal and first wife of Iran’s Shah Mohammad Reza Pahlavi.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fawzia
Target entity description: Fawzia is a feminine given name most famously borne by Princess Fawzia of Egypt, a 20th-century Egyptian royal and first wife of Iran’s Shah Mohammad Reza Pahlavi.
  • A. Habiba
    Habiba is a feminine given name commonly used in Arabic-speaking and Muslim-majority cultures, meaning "beloved" or "darling."
  • B. 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.
  • C. Rashida
    Rashida is a character featured in the film "Out of the Game."
  • D. Aziza
    Aziza is a traditional deity revered in Urhobo religion, associated with spiritual protection and guidance within the culture of the Urhobo people of Nigeria.
  • E. Khanum
    Khanum is a historical Turkic and Mongol honorific title used for noblewomen or female rulers, roughly equivalent to "queen" or "lady."
  • 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_69c00849d55481908b4f9f5543e0bf6d completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0346ac31c8190bbd28444f75da875 completed March 22, 2026, 6:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c113464f7481909538a1f2ef05216c completed March 23, 2026, 10:17 a.m.
NEDg Description generation batch_69c113de5d788190affa46cf416d180d completed March 23, 2026, 10:20 a.m.
NED2 Entity disambiguation (via description) batch_69c1144e77f881908ab59a67160c1630 completed March 23, 2026, 10:22 a.m.
Created at: March 22, 2026, 3:54 p.m.