Triple
T12800599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helene Bresslau |
E306006
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Lambaréné |
E245957
|
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: Lambaréné | Statement: [Helene Bresslau, residence, Lambaréné]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lambaréné Context triple: [Helene Bresslau, residence, Lambaréné]
-
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.
Butembo
Butembo is a major commercial city in eastern Democratic Republic of the Congo, known as a trading hub and economic center in North Kivu.
-
C.
Moanda
Moanda is a major mining town in southeastern Gabon known for its rich manganese deposits and role in the country’s extractive industry.
-
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.
Fougamou
Fougamou is a small town in southwestern Gabon that serves as an administrative and transport hub in Ngounié Province.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e7d3f5c8190bf01bef5d263ca26 |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ec0d5dc819099a8036c6cbac634 |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 9, 2026, 5:30 p.m.