Triple
T3238060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arba'a Rukun Mosque |
E67900
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Benadir |
E340408
|
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: Benadir | Statement: [Arba'a Rukun Mosque, region, Benadir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benadir Context triple: [Arba'a Rukun Mosque, region, Benadir]
-
A.
Benadir
chosen
Benadir is a coastal region in southeastern Somalia centered around the capital city Mogadishu, historically known as an important hub of Indian Ocean trade and Islamic culture.
-
B.
Anseba
Anseba is a central region of Eritrea known for its diverse ethnic communities, agriculture, and the regional capital Keren.
-
C.
Berbera
Berbera is a major port city on the Gulf of Aden in Somaliland, serving as a key maritime hub for trade in the Horn of Africa.
-
D.
Buhera
Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
-
E.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef3b04081908ce9b788e2e5c63c |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28ea696a08190a17cbeeef7632977 |
completed | March 12, 2026, 10 a.m. |
Created at: March 8, 2026, 3:08 p.m.