Triple
T13770182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uganda kob |
E330859
|
entity |
| Predicate | symbolOf |
P129
|
FINISHED |
| Object | Uganda |
E10768
|
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: Uganda | Statement: [Uganda kob, symbolOf, Uganda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uganda Context triple: [Uganda kob, symbolOf, Uganda]
-
A.
Uganda
chosen
Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
-
B.
Uganda and Democratic Republic of the Congo
Uganda and the Democratic Republic of the Congo are neighboring Central-East African countries that share extensive natural frontiers, rich biodiversity, and significant cross-border cultural and economic ties.
-
C.
Oluganda
Oluganda is the endonym for Luganda, a major Bantu language spoken primarily by the Baganda people in central Uganda.
-
D.
Nzera
Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
-
E.
Kenya
Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0233ecc48190b934f085d2501eb1 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b0676e50819085fe48f86c0f93d8 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 10:10 p.m.