Triple
T10247168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Libreville |
E240245
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Libreville metropolitan area |
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 metropolitan area | Statement: [Port of Libreville, serves, Libreville metropolitan area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libreville metropolitan area Context triple: [Port of Libreville, serves, Libreville metropolitan area]
-
A.
Libreville
chosen
Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
-
B.
Port-Gentil
Port-Gentil is Gabon's second-largest city and a major oil and port hub located on the country's Atlantic coast.
-
C.
Beni Douala
Beni Douala is a town and commune in northern Algeria, situated in the Kabylie region within Tizi Ouzou Province.
-
D.
Limbé
Limbé is a historic town in northern Haiti known for its agricultural surroundings and role in the country’s colonial and revolutionary past.
-
E.
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.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22e0d4c8190a6712859924e9d3d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d66ae248190b8af31b032f9f857 |
completed | April 10, 2026, 2:47 p.m. |
Created at: April 6, 2026, 11:27 a.m.