Triple
T6592891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bangui |
E148403
|
entity |
| Predicate | roadConnectionTo |
P9041
|
FINISHED |
| Object |
Mbaïki
Mbaïki is a town in the Central African Republic that serves as an important regional center southwest of the capital, Bangui.
|
E603466
|
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: Mbaïki | Statement: [Bangui, roadConnectionTo, Mbaïki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mbaïki Context triple: [Bangui, roadConnectionTo, Mbaïki]
-
A.
Mikongo
Mikongo is a small settlement in central Gabon that serves as a key access point for visitors exploring Lope National Park.
-
B.
Mungaka
Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
-
C.
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.
-
D.
Mbanderu
Mbanderu is a subgroup of the Herero people with its own distinct dialect and cultural traditions, primarily found in Namibia and Botswana.
-
E.
Mbaitoli
Mbaitoli is a local government area in southeastern Nigeria known for its predominantly Igbo population and its role within Imo State’s administrative and cultural landscape.
- 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: Mbaïki Triple: [Bangui, roadConnectionTo, Mbaïki]
Generated description
Mbaïki is a town in the Central African Republic that serves as an important regional center southwest of the capital, Bangui.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mbaïki Target entity description: Mbaïki is a town in the Central African Republic that serves as an important regional center southwest of the capital, Bangui.
-
A.
Mikongo
Mikongo is a small settlement in central Gabon that serves as a key access point for visitors exploring Lope National Park.
-
B.
Mungaka
Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
-
C.
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.
-
D.
Mbanderu
Mbanderu is a subgroup of the Herero people with its own distinct dialect and cultural traditions, primarily found in Namibia and Botswana.
-
E.
Mbaitoli
Mbaitoli is a local government area in southeastern Nigeria known for its predominantly Igbo population and its role within Imo State’s administrative and cultural landscape.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6aecf50ac81909cb9960c8265a7ea |
completed | March 27, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d57d338c81909e935926d635d2fc |
completed | March 27, 2026, 7:07 p.m. |
| NEDg | Description generation | batch_69c6d677a74881908e174a6f6c7a5497 |
completed | March 27, 2026, 7:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6d8486534819080b75cad9cc32276 |
completed | March 27, 2026, 7:19 p.m. |
Created at: March 27, 2026, 1:55 p.m.