Triple
T8698658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kandake |
E206465
|
entity |
| Predicate | notableBearer |
P458
|
FINISHED |
| Object |
Amanishakheto
Amanishakheto was a powerful queen of the ancient Kingdom of Kush, known for her military leadership and richly adorned royal tomb at Meroë.
|
E750912
|
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: Amanishakheto | Statement: [Kandake, notableBearer, Amanishakheto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanishakheto Context triple: [Kandake, notableBearer, Amanishakheto]
-
A.
Sekhen
Sekhen is an alternative name for the ancient Egyptian king Ka, an early ruler of the First Dynasty period.
-
B.
Waset
Waset was the ancient Egyptian city known in Greek as Thebes, a major religious and political center along the Nile.
-
C.
Napatan Kush
Napatan Kush was an ancient Nubian kingdom centered at Napata that flourished along the Nile in what is now Sudan, known for its powerful rulers, pyramids, and close cultural ties with ancient Egypt.
-
D.
Qurna
Qurna is a town in southern Iraq near the confluence of the Tigris and Euphrates rivers, historically significant as a strategic site during World War I.
-
E.
Saqar
Saqar is a term in the Qur'an referring to a severe level of Hell associated with intense punishment for disbelievers.
- 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: Amanishakheto Triple: [Kandake, notableBearer, Amanishakheto]
Generated description
Amanishakheto was a powerful queen of the ancient Kingdom of Kush, known for her military leadership and richly adorned royal tomb at Meroë.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amanishakheto Target entity description: Amanishakheto was a powerful queen of the ancient Kingdom of Kush, known for her military leadership and richly adorned royal tomb at Meroë.
-
A.
Sekhen
Sekhen is an alternative name for the ancient Egyptian king Ka, an early ruler of the First Dynasty period.
-
B.
Waset
Waset was the ancient Egyptian city known in Greek as Thebes, a major religious and political center along the Nile.
-
C.
Napatan Kush
Napatan Kush was an ancient Nubian kingdom centered at Napata that flourished along the Nile in what is now Sudan, known for its powerful rulers, pyramids, and close cultural ties with ancient Egypt.
-
D.
Qurna
Qurna is a town in southern Iraq near the confluence of the Tigris and Euphrates rivers, historically significant as a strategic site during World War I.
-
E.
Saqar
Saqar is a term in the Qur'an referring to a severe level of Hell associated with intense punishment for disbelievers.
- 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_69ca83555b6c8190abe930dd397e863b |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58af50408190a3b81100a759795e |
completed | March 31, 2026, 11:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef40e7a2881909e2d7eee0d931992 |
completed | April 2, 2026, 10:56 p.m. |
| NEDg | Description generation | batch_69cef66624348190b1f922b8148553a2 |
completed | April 2, 2026, 11:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cef6d9ea7c81909dfefb8540636092 |
completed | April 2, 2026, 11:08 p.m. |
Created at: March 30, 2026, 6:34 p.m.