Triple
T9248494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Guinea |
E222257
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Kouroussa
Kouroussa is a town in eastern Guinea known as a regional trading center and river port on the Niger River.
|
E786520
|
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: Kouroussa | Statement: [Upper Guinea, containsCity, Kouroussa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kouroussa Context triple: [Upper Guinea, containsCity, Kouroussa]
-
A.
Bachué
Bachué is a principal mother goddess in Muisca mythology, associated with creation, fertility, and the origin of humanity.
-
B.
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.
-
C.
Kabuna
Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
-
D.
Machar
Machar is a small rural township in Ontario, Canada, known for its forests, lakes, and low-density residential and agricultural character.
-
E.
Dja-et-Lobo
Dja-et-Lobo is a department in the South Region of Cameroon known for its largely forested landscape and low population density.
- 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: Kouroussa Triple: [Upper Guinea, containsCity, Kouroussa]
Generated description
Kouroussa is a town in eastern Guinea known as a regional trading center and river port on the Niger River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kouroussa Target entity description: Kouroussa is a town in eastern Guinea known as a regional trading center and river port on the Niger River.
-
A.
Bachué
Bachué is a principal mother goddess in Muisca mythology, associated with creation, fertility, and the origin of humanity.
-
B.
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.
-
C.
Kabuna
Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
-
D.
Machar
Machar is a small rural township in Ontario, Canada, known for its forests, lakes, and low-density residential and agricultural character.
-
E.
Dja-et-Lobo
Dja-et-Lobo is a department in the South Region of Cameroon known for its largely forested landscape and low population density.
- 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_69ca841d2b18819089f9faf5b2c2aec0 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd05f6d62c8190a1e33f1854767b47 |
completed | April 1, 2026, 11:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d077fed7888190a5d36bc2ee4c2bd2 |
completed | April 4, 2026, 2:31 a.m. |
| NEDg | Description generation | batch_69d0787b68ac819094acdec0ad7462ac |
completed | April 4, 2026, 2:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d07900c7588190869c26dc76fe97e7 |
completed | April 4, 2026, 2:35 a.m. |
Created at: March 30, 2026, 7:31 p.m.