Triple
T19659259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burao |
E472035
|
entity |
| Predicate | roadConnection |
P385
|
FINISHED |
| Object | Berbera |
—
|
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: Berbera | Statement: [Burao, roadConnection, Berbera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berbera Context triple: [Burao, roadConnection, Berbera]
-
A.
Berbera
chosen
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.
-
B.
Banyole
The Banyole are a Bantu-speaking ethnic group in eastern Uganda known for their agricultural livelihoods, clan-based social structure, and rich oral traditions.
-
C.
de Zogheb
de Zogheb is a surname most notably associated with Anne de Zogheb, a former fashion model and the ex-wife of singer Paul Anka.
-
D.
Bardera
Bardera is a major Somali city in the Gedo region, serving as an important commercial and administrative center in southwestern Somalia.
-
E.
Melesse
Melesse is a commune in the Ille-et-Vilaine department of Brittany in northwestern France.
- 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e641485ce481908b3860fa5e3a9f6e |
completed | April 20, 2026, 3:07 p.m. |
Created at: April 10, 2026, 1:45 p.m.