Triple
T20273320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Schweitzer of Lambaréné |
E502945
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Lambaréné, Gabon |
—
|
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: Lambaréné, Gabon | Statement: [Dr. Schweitzer of Lambaréné, setting, Lambaréné, Gabon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lambaréné, Gabon Context triple: [Dr. Schweitzer of Lambaréné, setting, Lambaréné, Gabon]
-
A.
Lambaréné
chosen
Lambaréné is a town in western Gabon best known for its location on the Ogooué River and for hosting the historic Albert Schweitzer Hospital.
-
B.
Limbé
Limbé is a historic town in northern Haiti known for its agricultural surroundings and role in the country’s colonial and revolutionary past.
-
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.
Pointe-Noire
Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial centers.
-
E.
Kribi
Kribi is a coastal resort town in southern Cameroon known for its sandy beaches, fishing port, and proximity to the Chutes de la Lobé waterfalls.
- 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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e675dff3c4819098ba45eba4e7b296 |
completed | April 20, 2026, 6:52 p.m. |
Created at: April 16, 2026, 10:20 a.m.