Triple
T698486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dakar |
E13945
|
entity |
| Predicate | country |
P26
|
FINISHED |
| Object | Senegal |
E36946
|
NE FINISHED |
How this triple was built (2 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: Senegal | Statement: [Dakar, country, Senegal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senegal Context triple: [Dakar, country, Senegal]
-
A.
Senegal
chosen
Senegal is a West African country on the Atlantic coast known for its vibrant culture, historic role in transatlantic trade, and diverse coastal and Sahelian landscapes.
-
B.
The Gambia
The Gambia is a small West African country centered around the Gambia River, known for its diverse ecosystems, colonial history, and tourism-focused economy.
-
C.
Mali
Mali is a landlocked West African country known for its historic trading cities like Timbuktu, rich Sahelian culture, and significant role in the ancient Mali Empire.
-
D.
Mauritania
Mauritania is a Northwest African country on the Atlantic coast, known for its vast Saharan landscapes, mixed Arab-Berber and Sub-Saharan cultures, and significant iron ore resources.
-
E.
Guinea
Guinea is a West African country on the Atlantic coast known for its rich mineral resources, diverse ethnic groups, and role as a major producer of bauxite.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c99be48190babc37c397b6a186 |
completed | March 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf39f5694819086fcdbd27fa2ea4b |
completed | March 8, 2026, 10:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.