Triple
T19579102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | N'Djamena International Airport |
E489938
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | N'Djamena |
—
|
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: N'Djamena | Statement: [N'Djamena International Airport, cityServed, N'Djamena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: N'Djamena Context triple: [N'Djamena International Airport, cityServed, N'Djamena]
-
A.
N'Djamena
chosen
N'Djamena is the largest city and political, economic, and cultural center of Chad, located in the southwestern part of the country near the border with Cameroon.
-
B.
Ouaga
Ouaga is the commonly used short name for Ouagadougou, the capital and largest city of Burkina Faso.
-
C.
Moundou
Moundou is a major city in southwestern Chad and an important industrial and commercial center, particularly known for its cotton and oil industries.
-
D.
Abéché
Abéché is a major city in eastern Chad that serves as an important regional trade and administrative center.
-
E.
Bangui
Bangui is the capital and largest city of the Central African Republic, serving as its political, economic, and cultural center.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6402693d88190a828c0e136895783 |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.