Triple
T14554631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uzun Hasan |
E341505
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Khalil Mirza
Khalil Mirza was a historical figure of the Aq Qoyunlu dynasty, known primarily as a son of the Turkmen ruler Uzun Hasan.
|
E1106363
|
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: Khalil Mirza | Statement: [Uzun Hasan, child, Khalil Mirza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Khalil Mirza Context triple: [Uzun Hasan, child, Khalil Mirza]
-
A.
Mehdi Mirza
Mehdi Mirza is a machine learning researcher known for his contributions to deep reinforcement learning and generative models.
-
B.
Amar Khalil
Amar Khalil is an American R&B singer best known for his work with the influential Oakland-based group Tony! Toni! Toné!.
-
C.
Azam Khan
Azam Khan is an Indian politician and founding member of the Samajwadi Party, known for his long tenure as a legislator from Uttar Pradesh and his influential role in state politics.
-
D.
Mohammad Azar
Mohammad Azar is a machine learning researcher known for co-authoring the influential Rainbow DQN algorithm in deep reinforcement learning.
-
E.
Talat Hussain
Talat Hussain was a prominent Pakistani actor and voice artist known for his work in film, television, and radio in Pakistan and abroad.
- 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: Khalil Mirza Triple: [Uzun Hasan, child, Khalil Mirza]
Generated description
Khalil Mirza was a historical figure of the Aq Qoyunlu dynasty, known primarily as a son of the Turkmen ruler Uzun Hasan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Khalil Mirza Target entity description: Khalil Mirza was a historical figure of the Aq Qoyunlu dynasty, known primarily as a son of the Turkmen ruler Uzun Hasan.
-
A.
Mehdi Mirza
Mehdi Mirza is a machine learning researcher known for his contributions to deep reinforcement learning and generative models.
-
B.
Amar Khalil
Amar Khalil is an American R&B singer best known for his work with the influential Oakland-based group Tony! Toni! Toné!.
-
C.
Azam Khan
Azam Khan is an Indian politician and founding member of the Samajwadi Party, known for his long tenure as a legislator from Uttar Pradesh and his influential role in state politics.
-
D.
Mohammad Azar
Mohammad Azar is a machine learning researcher known for co-authoring the influential Rainbow DQN algorithm in deep reinforcement learning.
-
E.
Talat Hussain
Talat Hussain was a prominent Pakistani actor and voice artist known for his work in film, television, and radio in Pakistan and abroad.
- 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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb2f00cec8190a7b6482d18b9a216 |
completed | April 14, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ab9a5ac81908779a3c8701353fa |
completed | May 8, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_69fd8be7d8988190807d4db477b91de0 |
completed | May 8, 2026, 7:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8d4f2e848190a3c4c423c0ffed50 |
completed | May 8, 2026, 7:14 a.m. |
Created at: April 10, 2026, 1:23 a.m.