Triple
T30985169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Simingaye Zulu |
E789503
|
entity |
| Predicate | notableFamily |
P1481
|
FINISHED |
| Object |
House of Zulu
The House of Zulu is the royal dynasty of the Zulu people, historically led by Zulu kings who have played a central role in the politics and culture of what is now South Africa.
|
E538303
|
NE FINISHED |
How this triple was built (2 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: House of Zulu | Statement: [Prince Simingaye Zulu, notableFamily, House of Zulu]
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: House of Zulu Triple: [Prince Simingaye Zulu, notableFamily, House of Zulu]
Generated description
The House of Zulu is the royal dynasty of the Zulu people, historically led by Zulu kings who have played a central role in the politics and culture of what is now South Africa.
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_69f224c550b081909ddfceb0c3d03bdd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f693c1b9f88190a439254f342c1679 |
completed | May 3, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a296bc3aa0881909eda108125f560f2 |
completed | June 10, 2026, 1:50 p.m. |
| NEDg | Description generation | batch_6a296d4e7e0481908c6aa4bbfca0b520 |
completed | June 10, 2026, 1:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a299ba2c2a4819090e463dcae50684f |
completed | June 10, 2026, 5:15 p.m. |
Created at: April 29, 2026, 8:56 p.m.