Triple
T22765583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adamawa Region |
E563113
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Banyo |
—
|
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: Banyo | Statement: [Adamawa Region, containsCity, Banyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Banyo Context triple: [Adamawa Region, containsCity, Banyo]
-
A.
Banyo
chosen
Banyo is a town and commune in the Adamawa Region of Cameroon known as a local administrative and trading center.
-
B.
Arganzuela
Arganzuela is a central district of Madrid, Spain, known for its extensive redevelopment along the Manzanares River and its mix of residential areas, cultural venues, and green spaces.
-
C.
Bijuesca
Bijuesca is a small municipality in the province of Zaragoza, in the autonomous community of Aragon, Spain.
-
D.
Baños
Baños is a popular tourist town in central Ecuador known for its hot springs, waterfalls, and adventure sports.
-
E.
Bacalar
Bacalar is a picturesque town in Mexico’s Quintana Roo state, best known for its stunning multi-hued “Lagoon of Seven Colors” and tranquil, less-touristed atmosphere.
- 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_69e24552e11c81909c2d61578a558bd7 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17a80249c819091569e7b8d500b45 |
completed | April 29, 2026, 3:26 a.m. |
Created at: April 17, 2026, 3:26 p.m.