Triple
T23360750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ring Road (Northwest Cameroon) |
E593175
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Babessi |
—
|
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: Babessi | Statement: [Ring Road (Northwest Cameroon), connects, Babessi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Babessi Context triple: [Ring Road (Northwest Cameroon), connects, Babessi]
-
A.
Babessi
chosen
Babessi is a town and commune in Cameroon’s Northwest Region, known as part of the Ndop Plain and for its predominantly agrarian communities.
-
B.
Babun
Babun is a central character from the Sri Lankan novel and film "Beddegama" ("The Village in the Jungle"), representing the struggles of rural villagers under colonial-era hardship and injustice.
-
C.
Baniata
Baniata is an Oceanic language of the Meso-Melanesian group spoken in the Solomon Islands.
-
D.
Ebebiyín
Ebebiyín is a town in northeastern Equatorial Guinea, near the borders with Cameroon and Gabon, known as an important regional and commercial center.
-
E.
Bisha
Bisha is a major inland city in southwestern Saudi Arabia known for its agricultural production and strategic location within the Asir region.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a0a730f8819088fec53a43b063f8 |
completed | April 29, 2026, 6:09 a.m. |
Created at: April 17, 2026, 5:30 p.m.