Triple
T10586580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toposa language |
E249869
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Kiyala Toposa
Kiyala Toposa is a regional dialect of the Toposa language spoken by Toposa communities in South Sudan.
|
E873782
|
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: Kiyala Toposa | Statement: [Toposa language, hasDialect, Kiyala Toposa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiyala Toposa Context triple: [Toposa language, hasDialect, Kiyala Toposa]
-
A.
Kidepo Toposa
Kidepo Toposa is a regional dialect of the Toposa language spoken by Toposa communities in the Kidepo area of South Sudan.
-
B.
Nabaloi
Nabaloi is an Austronesian language spoken by the Ibaloi people of the northern Philippines, particularly in the Benguet region of Luzon.
-
C.
Mandura Gumuz
Mandura Gumuz is a Gumuz language spoken by the Gumuz people of western Ethiopia.
-
D.
Kasangati
Kasangati is a town in central Uganda that serves as a growing commercial and residential hub within the Greater Kampala metropolitan area.
-
E.
Matsigenka
The Matsigenka are an Indigenous people of the Peruvian Amazon known for their forest-based subsistence lifestyle, distinct language, and rich shamanic and cosmological traditions.
- 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: Kiyala Toposa Triple: [Toposa language, hasDialect, Kiyala Toposa]
Generated description
Kiyala Toposa is a regional dialect of the Toposa language spoken by Toposa communities in South Sudan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kiyala Toposa Target entity description: Kiyala Toposa is a regional dialect of the Toposa language spoken by Toposa communities in South Sudan.
-
A.
Kidepo Toposa
Kidepo Toposa is a regional dialect of the Toposa language spoken by Toposa communities in the Kidepo area of South Sudan.
-
B.
Nabaloi
Nabaloi is an Austronesian language spoken by the Ibaloi people of the northern Philippines, particularly in the Benguet region of Luzon.
-
C.
Mandura Gumuz
Mandura Gumuz is a Gumuz language spoken by the Gumuz people of western Ethiopia.
-
D.
Kasangati
Kasangati is a town in central Uganda that serves as a growing commercial and residential hub within the Greater Kampala metropolitan area.
-
E.
Matsigenka
The Matsigenka are an Indigenous people of the Peruvian Amazon known for their forest-based subsistence lifestyle, distinct language, and rich shamanic and cosmological traditions.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5276b0ae48190b2935230363239e0 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e7df88081908e3d77f357f176e3 |
completed | April 10, 2026, 8:33 p.m. |
| NEDg | Description generation | batch_69d95f97669881908b54ebcd1a7c6b34 |
completed | April 10, 2026, 8:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9602748608190b0c971accf44b7aa |
completed | April 10, 2026, 8:40 p.m. |
Created at: April 6, 2026, 12:39 p.m.