Triple
T6106137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Region |
E136121
|
entity |
| Predicate | hasLargestCity |
P235
|
FINISHED |
| Object | Ebolowa |
E568529
|
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: Ebolowa | Statement: [South Region, hasLargestCity, Ebolowa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ebolowa Context triple: [South Region, hasLargestCity, Ebolowa]
-
A.
Ebolowa
chosen
Ebolowa is a city in southern Cameroon that serves as an administrative and commercial center for the surrounding agricultural region.
-
B.
Benina
Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
-
C.
Abéché
Abéché is a major city in eastern Chad that serves as an important regional trade and administrative center.
-
D.
Ewondo
Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
-
E.
Calabar
Calabar is a historic port city in southeastern Nigeria known for its role in the transatlantic slave trade and its vibrant cultural festivals.
- 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_69c0087dee9881909e3655be88208c01 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05b7ee8b48190b87f5ec8a46d6e2d |
completed | March 22, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1359334c081909653603633ba9c06 |
completed | March 23, 2026, 12:44 p.m. |
Created at: March 22, 2026, 4:13 p.m.