Triple
T6435994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southwest Region (Cameroon) |
E129895
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Bangem
Bangem is a small town in western Cameroon that serves as the administrative center of Kupe-Muanenguba Division, known for its proximity to the Muanenguba Mountains and surrounding forested highlands.
|
E593181
|
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: Bangem | Statement: [Southwest Region (Cameroon), containsTown, Bangem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bangem Context triple: [Southwest Region (Cameroon), containsTown, Bangem]
-
A.
Banggae
Banggae is an alternative name for the Banggai language, an Austronesian language spoken in the Banggai Islands of Indonesia.
-
B.
Bangangté
Bangangté is a prominent city in western Cameroon known as an important administrative and commercial center of the West Region.
-
C.
Bombo Beach
Bombo Beach is a popular surf and swimming beach near Kiama on the New South Wales South Coast, known for its striking rock formations and coastal scenery.
-
D.
Bamble
Bamble is a coastal municipality in Vestfold og Telemark county in southeastern Norway.
-
E.
Bittrich
Bittrich is a German surname most notably borne by Wilhelm Bittrich, a high-ranking Waffen-SS commander during World War II.
- 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: Bangem Triple: [Southwest Region (Cameroon), containsTown, Bangem]
Generated description
Bangem is a small town in western Cameroon that serves as the administrative center of Kupe-Muanenguba Division, known for its proximity to the Muanenguba Mountains and surrounding forested highlands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bangem Target entity description: Bangem is a small town in western Cameroon that serves as the administrative center of Kupe-Muanenguba Division, known for its proximity to the Muanenguba Mountains and surrounding forested highlands.
-
A.
Banggae
Banggae is an alternative name for the Banggai language, an Austronesian language spoken in the Banggai Islands of Indonesia.
-
B.
Bangangté
Bangangté is a prominent city in western Cameroon known as an important administrative and commercial center of the West Region.
-
C.
Bombo Beach
Bombo Beach is a popular surf and swimming beach near Kiama on the New South Wales South Coast, known for its striking rock formations and coastal scenery.
-
D.
Bamble
Bamble is a coastal municipality in Vestfold og Telemark county in southeastern Norway.
-
E.
Bittrich
Bittrich is a German surname most notably borne by Wilhelm Bittrich, a high-ranking Waffen-SS commander during World War II.
- 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_69c640f2915c8190aea3578dcd77dd5f |
completed | March 27, 2026, 8:33 a.m. |
| NEDg | Description generation | batch_69c64237ae8881908bbaa2760113da7c |
completed | March 27, 2026, 8:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c64658463c8190a1d68beec15cab3d |
completed | March 27, 2026, 8:56 a.m. |
Created at: March 22, 2026, 4:45 p.m.