Triple
T20072123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanganyika Province |
E499762
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Kongolo |
—
|
NE NERFINISHED |
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: Kongolo | Statement: [Tanganyika Province, hasCity, Kongolo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kongolo Context triple: [Tanganyika Province, hasCity, Kongolo]
-
A.
Kongolo
chosen
Kongolo is a town in the Tanganyika Province of the Democratic Republic of the Congo, situated along the Lukuga River and serving as a local transport and trading hub.
-
B.
Luba-Kasai
Luba-Kasai is a Bantu language spoken primarily in the Kasai region of the Democratic Republic of the Congo by the Luba people.
-
C.
Konongo
Konongo is a prominent mining and commercial town in central Ghana known historically for its gold deposits and later manganese production.
-
D.
Lualaba River
The Lualaba River is the upper course of the Congo River in the Democratic Republic of the Congo, flowing through the southeast of the country and serving as a key waterway in Central Africa.
-
E.
Kongō
Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66438633481908710907c48806499 |
completed | April 20, 2026, 5:36 p.m. |
Created at: April 11, 2026, 3:40 p.m.