Triple
T5845726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garoua |
E129704
|
entity |
| Predicate | connectedByRoadTo |
P11435
|
FINISHED |
| Object | Maroua |
E141174
|
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: Maroua | Statement: [Garoua, connectedByRoadTo, Maroua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maroua Context triple: [Garoua, connectedByRoadTo, Maroua]
-
A.
Maroua
chosen
Maroua is a prominent city in northern Cameroon known as a regional commercial and cultural center near the Sahel.
-
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.
Ubangi-Shari
Ubangi-Shari was the French colonial territory in central Africa that later became the independent nation of the Central African Republic.
-
D.
Eaux-Bonnes
Eaux-Bonnes is a spa and mountain resort village in the French Pyrenees, known for its thermal baths and scenic alpine surroundings.
-
E.
Abéché
Abéché is a major city in eastern Chad that serves as an important regional trade and administrative center.
- 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c034dcafe88190a438034a539ffa52 |
completed | March 22, 2026, 6:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b0fb0f68819091018926ee4c7bb8 |
completed | March 23, 2026, 3:18 a.m. |
Created at: March 22, 2026, 3:55 p.m.