Triple
T6435991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southwest Region (Cameroon) |
E129895
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Ekondo-Titi
Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
|
E593389
|
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: Ekondo-Titi | Statement: [Southwest Region (Cameroon), containsCity, Ekondo-Titi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ekondo-Titi Context triple: [Southwest Region (Cameroon), containsCity, Ekondo-Titi]
-
A.
Butembo
Butembo is a major commercial city in eastern Democratic Republic of the Congo, known as a trading hub and economic center in North Kivu.
-
B.
Ebolowa
Ebolowa is a city in southern Cameroon that serves as an administrative and commercial center for the surrounding agricultural region.
-
C.
Benina
Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
-
D.
Ebanga
Ebanga is a monoclonal antibody drug used to treat Zaire ebolavirus infection (Ebola virus disease).
-
E.
Kié-Ntem
Kié-Ntem is a province in mainland Equatorial Guinea known for its largely forested landscapes and border location with Cameroon and Gabon.
- 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: Ekondo-Titi Triple: [Southwest Region (Cameroon), containsCity, Ekondo-Titi]
Generated description
Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ekondo-Titi Target entity description: Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
-
A.
Butembo
Butembo is a major commercial city in eastern Democratic Republic of the Congo, known as a trading hub and economic center in North Kivu.
-
B.
Ebolowa
Ebolowa is a city in southern Cameroon that serves as an administrative and commercial center for the surrounding agricultural region.
-
C.
Benina
Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
-
D.
Ebanga
Ebanga is a monoclonal antibody drug used to treat Zaire ebolavirus infection (Ebola virus disease).
-
E.
Kié-Ntem
Kié-Ntem is a province in mainland Equatorial Guinea known for its largely forested landscapes and border location with Cameroon and Gabon.
- 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c069622eb881908b40fc8079d312d6 |
completed | March 22, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bbf31bc8190981362639a0e1ce5 |
completed | March 27, 2026, 9:19 a.m. |
| NEDg | Description generation | batch_69c64c467d4881909a2bb21e64ed8962 |
completed | March 27, 2026, 9:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c64cb64a78819086a84cf36bb06a1b |
completed | March 27, 2026, 9:24 a.m. |
Created at: March 22, 2026, 4:45 p.m.