Triple
T899748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benue River |
E19419
|
entity |
| Predicate | hasTributary |
P415
|
FINISHED |
| Object |
Mayo Kébbi
Mayo Kébbi is a river in Central Africa that serves as a key waterway in Chad and Cameroon, contributing to the regional drainage system connected to the Benue River.
|
E106867
|
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: Mayo Kébbi | Statement: [Benue River, hasTributary, Mayo Kébbi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayo Kébbi Context triple: [Benue River, hasTributary, Mayo Kébbi]
-
A.
Ndowe
Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
-
B.
Musa
Musa is the name used in the Quran for the prophet Moses, a central figure in Islamic tradition known for leading the Israelites and receiving divine revelation.
-
C.
Mandinka
Mandinka is a major Mande language spoken primarily in The Gambia, Senegal, Guinea-Bissau, and neighboring West African countries by the Mandinka people.
-
D.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
E.
Idrissa
Idrissa is the given first name of British actor, producer, and musician Idris Elba.
- 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: Mayo Kébbi Triple: [Benue River, hasTributary, Mayo Kébbi]
Generated description
Mayo Kébbi is a river in Central Africa that serves as a key waterway in Chad and Cameroon, contributing to the regional drainage system connected to the Benue River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mayo Kébbi Target entity description: Mayo Kébbi is a river in Central Africa that serves as a key waterway in Chad and Cameroon, contributing to the regional drainage system connected to the Benue River.
-
A.
Ndowe
Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
-
B.
Musa
Musa is the name used in the Quran for the prophet Moses, a central figure in Islamic tradition known for leading the Israelites and receiving divine revelation.
-
C.
Mandinka
Mandinka is a major Mande language spoken primarily in The Gambia, Senegal, Guinea-Bissau, and neighboring West African countries by the Mandinka people.
-
D.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
E.
Idrissa
Idrissa is the given first name of British actor, producer, and musician Idris Elba.
- 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad4162848190aa2787b2fa3e6575 |
completed | March 1, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c734e680819098840e9c736b5ead |
completed | March 4, 2026, 5:46 a.m. |
| NEDg | Description generation | batch_69a7c8a3064081908772ee2305bbe3e1 |
completed | March 4, 2026, 5:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7c8fecaac8190a7b1a1cd2fa98a2d |
completed | March 4, 2026, 5:54 a.m. |
Created at: March 1, 2026, 7:39 p.m.