Triple
T6106147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Region |
E136121
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Kribi
Kribi is a coastal resort town in southern Cameroon known for its sandy beaches, fishing port, and proximity to the Chutes de la Lobé waterfalls.
|
E568530
|
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: Kribi | Statement: [South Region, containsCity, Kribi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kribi Context triple: [South Region, containsCity, Kribi]
-
A.
Maroua
Maroua is a prominent city in northern Cameroon known as a regional commercial and cultural center near the Sahel.
-
B.
Abéché
Abéché is a major city in eastern Chad that serves as an important regional trade and administrative center.
-
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.
Pointe-Noire
Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial centers.
-
E.
Ngaoundéré
Ngaoundéré is a major city in northern Cameroon that serves as the regional capital of Adamawa and an important commercial and transport hub between central and northern Africa.
- 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: Kribi Triple: [South Region, containsCity, Kribi]
Generated description
Kribi is a coastal resort town in southern Cameroon known for its sandy beaches, fishing port, and proximity to the Chutes de la Lobé waterfalls.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kribi Target entity description: Kribi is a coastal resort town in southern Cameroon known for its sandy beaches, fishing port, and proximity to the Chutes de la Lobé waterfalls.
-
A.
Maroua
Maroua is a prominent city in northern Cameroon known as a regional commercial and cultural center near the Sahel.
-
B.
Abéché
Abéché is a major city in eastern Chad that serves as an important regional trade and administrative center.
-
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.
Pointe-Noire
Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial centers.
-
E.
Ngaoundéré
Ngaoundéré is a major city in northern Cameroon that serves as the regional capital of Adamawa and an important commercial and transport hub between central and northern Africa.
- 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_69c0087dee9881909e3655be88208c01 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05b806bd48190b6f020af3391adb8 |
completed | March 22, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1255759f48190a6aadf33406dcb49 |
completed | March 23, 2026, 11:34 a.m. |
| NEDg | Description generation | batch_69c1275910108190a0a5f458a468c292 |
completed | March 23, 2026, 11:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c127b831d081909436a62e002d1fa5 |
completed | March 23, 2026, 11:44 a.m. |
Created at: March 22, 2026, 4:13 p.m.