Triple
T13680412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orientale Province |
E327984
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Wamba
Wamba is a town located in the northeastern part of the Democratic Republic of the Congo.
|
E1057950
|
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: Wamba | Statement: [Orientale Province, containsCity, Wamba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wamba Context triple: [Orientale Province, containsCity, Wamba]
-
A.
Wamba
Wamba is a town and administrative local government area in Nasarawa State, central Nigeria, known for its diverse ethnic communities and agricultural activities.
-
B.
Wamba
Wamba was a 7th-century king of the Visigoths in Hispania, known for his military campaigns and efforts to strengthen royal authority.
-
C.
Kenzi
Kenzi is a Nubian language spoken in southern Egypt, closely related to Nobiin and part of the broader Nubian language family along the Nile.
-
D.
Tembo
Tembo are a Bantu-speaking ethnic group primarily inhabiting the eastern region of the Democratic Republic of the Congo, known for their agrarian lifestyle and rich cultural traditions.
-
E.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of 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: Wamba Triple: [Orientale Province, containsCity, Wamba]
Generated description
Wamba is a town located in the northeastern part of the Democratic Republic of the Congo.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wamba Target entity description: Wamba is a town located in the northeastern part of the Democratic Republic of the Congo.
-
A.
Wamba
Wamba was a 7th-century king of the Visigoths in Hispania, known for his military campaigns and efforts to strengthen royal authority.
-
B.
Wamba
Wamba is a town and administrative local government area in Nasarawa State, central Nigeria, known for its diverse ethnic communities and agricultural activities.
-
C.
Kenzi
Kenzi is a Nubian language spoken in southern Egypt, closely related to Nobiin and part of the broader Nubian language family along the Nile.
-
D.
Tembo
Tembo are a Bantu-speaking ethnic group primarily inhabiting the eastern region of the Democratic Republic of the Congo, known for their agrarian lifestyle and rich cultural traditions.
-
E.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc66cbb088190907cb89dda8e4ebd |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d4a4a50819098bd4348eba19ee7 |
completed | May 3, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f7a15f3c908190be380355972def6e |
completed | May 3, 2026, 7:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a2234390819093814fd435f9c42c |
completed | May 3, 2026, 7:29 p.m. |
Created at: April 9, 2026, 9:53 p.m.