Triple
T19369794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Gondi |
E484500
|
entity |
| Predicate | hasDialects |
P4251
|
FINISHED |
| Object |
Mandla Gondi
Mandla Gondi is a regional dialect of the Northern Gondi language spoken by Gondi communities in and around the Mandla area of central India.
|
E1372622
|
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: Mandla Gondi | Statement: [Northern Gondi, hasDialects, Mandla Gondi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mandla Gondi Context triple: [Northern Gondi, hasDialects, Mandla Gondi]
-
A.
Mandla Gaduka
Mandla Gaduka is a South African actor best known internationally for his role in the science-fiction film "District 9."
-
B.
Manguisa
Manguisa is a Bantu language spoken by the Manguisa people in parts of Central Africa, particularly in Cameroon.
-
C.
Mahlamba Ndlopfu
Mahlamba Ndlopfu is the official presidential residence of South Africa, located in the Bryntirion Estate in Pretoria.
-
D.
Mncedisi Shabangu
Mncedisi Shabangu was a South African actor and theatre director known for his work in film, television, and stage productions.
-
E.
Mponeng Mhlauli
Mponeng Mhlauli is a South African writer whose experiences under apartheid inspired the story that became the basis for the Disney Channel film "The Color of Friendship."
- 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: Mandla Gondi Triple: [Northern Gondi, hasDialects, Mandla Gondi]
Generated description
Mandla Gondi is a regional dialect of the Northern Gondi language spoken by Gondi communities in and around the Mandla area of central India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mandla Gondi Target entity description: Mandla Gondi is a regional dialect of the Northern Gondi language spoken by Gondi communities in and around the Mandla area of central India.
-
A.
Mandla Gaduka
Mandla Gaduka is a South African actor best known internationally for his role in the science-fiction film "District 9."
-
B.
Manguisa
Manguisa is a Bantu language spoken by the Manguisa people in parts of Central Africa, particularly in Cameroon.
-
C.
Mahlamba Ndlopfu
Mahlamba Ndlopfu is the official presidential residence of South Africa, located in the Bryntirion Estate in Pretoria.
-
D.
Mncedisi Shabangu
Mncedisi Shabangu was a South African actor and theatre director known for his work in film, television, and stage productions.
-
E.
Mponeng Mhlauli
Mponeng Mhlauli is a South African writer whose experiences under apartheid inspired the story that became the basis for the Disney Channel film "The Color of Friendship."
- 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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e619af33e481908643f8beb2f498dc |
completed | April 20, 2026, 12:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a072419c4ec8190931c1918450f2a83 |
completed | May 15, 2026, 1:48 p.m. |
| NEDg | Description generation | batch_6a0725c32c4c8190bb29a764695cd245 |
completed | May 15, 2026, 1:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0726ea260c8190b97e2b18fde36272 |
completed | May 15, 2026, 2 p.m. |
Created at: April 10, 2026, 1:35 p.m.