Triple
T10722640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmanuel Issoze-Ngondet |
E252859
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Libreville |
E47980
|
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: Libreville | Statement: [Emmanuel Issoze-Ngondet, workLocation, Libreville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libreville Context triple: [Emmanuel Issoze-Ngondet, workLocation, Libreville]
-
A.
Libreville
chosen
Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
-
B.
Douala
Douala is the economic capital and main port city of Cameroon, located on the Wouri River along the Atlantic coast.
-
C.
Port-Gentil
Port-Gentil is Gabon's second-largest city and a major oil and port hub located on the country's Atlantic coast.
-
D.
Yaoundé
Yaoundé is the political and administrative center of Cameroon, known for its hilly terrain and role as a major cultural and economic hub in Central Africa.
-
E.
Limbé
Limbé is a historic town in northern Haiti known for its agricultural surroundings and role in the country’s colonial and revolutionary past.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d70d44d0048190a78aae2357e864a5 |
completed | April 9, 2026, 2:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e712b2ff1081908ccf311e1133ab72 |
completed | April 21, 2026, 6:01 a.m. |
Created at: April 8, 2026, 9:13 p.m.