Triple
T547163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babur |
E12757
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Hindal Mirza
Hindal Mirza was a Mughal prince, the youngest son of Emperor Babur and a notable figure in the early Mughal court and succession struggles.
|
E69288
|
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: Hindal Mirza | Statement: [Babur, child, Hindal Mirza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hindal Mirza Context triple: [Babur, child, Hindal Mirza]
-
A.
Hina Jilani
Hina Jilani is a prominent Pakistani lawyer and human rights activist known for her pioneering work in women's rights, civil liberties, and international justice.
-
B.
Shahrukh Mirza
Shahrukh Mirza was a 15th-century Timurid ruler who consolidated and governed much of Iran and Central Asia, fostering a flourishing of Persian culture, arts, and architecture.
-
C.
Hafeez Jalandhari
Hafeez Jalandhari was a Pakistani poet best known for writing the lyrics of Pakistan’s national anthem.
-
D.
Mirza
Mirza is a historical noble title of Persian and Central Asian origin, commonly borne by princes and high-ranking members of royal and aristocratic families.
-
E.
Tariq Anwar
Tariq Anwar is a British film editor known for his acclaimed work on numerous major films, including the Academy Award–winning drama "The King’s Speech."
- 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: Hindal Mirza Triple: [Babur, child, Hindal Mirza]
Generated description
Hindal Mirza was a Mughal prince, the youngest son of Emperor Babur and a notable figure in the early Mughal court and succession struggles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hindal Mirza Target entity description: Hindal Mirza was a Mughal prince, the youngest son of Emperor Babur and a notable figure in the early Mughal court and succession struggles.
-
A.
Hina Jilani
Hina Jilani is a prominent Pakistani lawyer and human rights activist known for her pioneering work in women's rights, civil liberties, and international justice.
-
B.
Shahrukh Mirza
Shahrukh Mirza was a 15th-century Timurid ruler who consolidated and governed much of Iran and Central Asia, fostering a flourishing of Persian culture, arts, and architecture.
-
C.
Hafeez Jalandhari
Hafeez Jalandhari was a Pakistani poet best known for writing the lyrics of Pakistan’s national anthem.
-
D.
Mirza
Mirza is a historical noble title of Persian and Central Asian origin, commonly borne by princes and high-ranking members of royal and aristocratic families.
-
E.
Tariq Anwar
Tariq Anwar is a British film editor known for his acclaimed work on numerous major films, including the Academy Award–winning drama "The King’s Speech."
- 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_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a498e2e8c88190a66d759ed3094e18 |
completed | March 1, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4e3f3f4e08190a625ae085868b191 |
completed | March 2, 2026, 1:12 a.m. |
| NEDg | Description generation | batch_69a4e47c4f908190969ff83c69d7c3a7 |
completed | March 2, 2026, 1:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a4e525eb18819083018d392ba4b2fa |
completed | March 2, 2026, 1:17 a.m. |
Created at: March 1, 2026, 7:32 p.m.