Triple

T16447565
Position Surface form Disambiguated ID Type / Status
Subject Mohammad Khodabanda E399470 entity
Predicate child P120 FINISHED
Object Hamza Mirza
Hamza Mirza was a Safavid prince of Iran, known primarily as a son of Shah Mohammad Khodabanda during the late 16th century.
E1214491 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: Hamza Mirza | Statement: [Mohammad Khodabanda, child, Hamza Mirza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hamza Mirza
Context triple: [Mohammad Khodabanda, child, Hamza Mirza]
  • A. Daniyal Mirza
    Daniyal Mirza was a Mughal prince of the 16th century, notable as one of Emperor Akbar's sons who played a role in the empire's dynastic politics.
  • B. Khalil Mirza
    Khalil Mirza was a historical figure of the Aq Qoyunlu dynasty, known primarily as a son of the Turkmen ruler Uzun Hasan.
  • C. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • D. Muhammad Muazzam
    Muhammad Muazzam, better known as Bahadur Shah I, was the seventh Mughal emperor of India who briefly ruled the empire from 1707 to 1712 following the death of his father Aurangzeb.
  • E. Shaheen Khan
    Shaheen Khan is a British actress best known for her role as the protagonist’s mother in the hit film "Bend It Like Beckham."
  • 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: Hamza Mirza
Triple: [Mohammad Khodabanda, child, Hamza Mirza]
Generated description
Hamza Mirza was a Safavid prince of Iran, known primarily as a son of Shah Mohammad Khodabanda during the late 16th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Hamza Mirza
Target entity description: Hamza Mirza was a Safavid prince of Iran, known primarily as a son of Shah Mohammad Khodabanda during the late 16th century.
  • A. Daniyal Mirza
    Daniyal Mirza was a Mughal prince of the 16th century, notable as one of Emperor Akbar's sons who played a role in the empire's dynastic politics.
  • B. Khalil Mirza
    Khalil Mirza was a historical figure of the Aq Qoyunlu dynasty, known primarily as a son of the Turkmen ruler Uzun Hasan.
  • C. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • D. Muhammad Muazzam
    Muhammad Muazzam, better known as Bahadur Shah I, was the seventh Mughal emperor of India who briefly ruled the empire from 1707 to 1712 following the death of his father Aurangzeb.
  • E. Shaheen Khan
    Shaheen Khan is a British actress best known for her role as the protagonist’s mother in the hit film "Bend It Like Beckham."
  • 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_69d87f2c6778819080fcfae53be8f12a completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32cddfc3c8190919b49f74b7e8e1a completed April 18, 2026, 7:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f4b738881908f8a205466397f33 completed May 10, 2026, 9:26 a.m.
NEDg Description generation batch_6a0050751be48190a76ab998b544ac5a completed May 10, 2026, 9:31 a.m.
NED2 Entity disambiguation (via description) batch_6a0050d99f7c81909a5d5582294790f7 completed May 10, 2026, 9:33 a.m.
Created at: April 10, 2026, 5:10 a.m.