Triple
T8938238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borkou |
E212831
|
entity |
| Predicate | country |
P26
|
FINISHED |
| Object | Chad |
E20731
|
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: Chad | Statement: [Borkou, country, Chad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chad Context triple: [Borkou, country, Chad]
-
A.
Chad
Chad is a common masculine given name used primarily in English-speaking countries.
-
B.
Chad
chosen
Chad is a landlocked country in north-central Africa known for its ethnic and linguistic diversity, vast desert regions, and significant oil reserves.
-
C.
Central African Republic
The Central African Republic is a landlocked, resource-rich but politically unstable country in Central Africa that has experienced recurrent conflict and humanitarian crises.
-
D.
Sudan
Sudan is a large Northeast African country along the Nile River, known for its diverse cultures, ancient Nubian history, and a modern history marked by civil conflict and the secession of South Sudan.
-
E.
Cameroon
Cameroon is a Central African country known for its cultural and linguistic diversity, varied geography from coast to rainforest and savanna, and a mixed French-English colonial heritage.
- 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66b57a348190979effe4f9998eb7 |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1e692548190b631c4926927d12f |
completed | April 3, 2026, 1:34 p.m. |
Created at: March 30, 2026, 6:58 p.m.