Triple
T21512225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BWh |
E530754
|
entity |
| Predicate | definedBy |
P773
|
FINISHED |
| Object | Wladimir Köppen |
—
|
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: Wladimir Köppen | Statement: [BWh, definedBy, Wladimir Köppen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wladimir Köppen Context triple: [BWh, definedBy, Wladimir Köppen]
-
A.
Wladimir Köppen
chosen
Wladimir Köppen was a Russian-German climatologist and geographer best known for creating the widely used Köppen climate classification system.
-
B.
Marie Köppen
Marie Köppen was the wife of renowned Russian-German climatologist Wladimir Köppen.
-
C.
Wolfgang Koeppen
Wolfgang Koeppen was a prominent 20th-century German novelist known for his psychologically rich, stylistically innovative works that critically examined postwar German society.
-
D.
Mikhail Budyko
Mikhail Budyko was a Soviet climatologist and pioneer of climate modeling whose work on energy balance and feedbacks in the Earth system helped lay the foundations for modern climate science.
-
E.
Ernst Cloos
Ernst Cloos was a German-American structural geologist known for his pioneering work in rock deformation and tectonics, and for his influential teaching at Johns Hopkins University.
- 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_69e0c45c81f08190a6b8bbb70a45aae7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea8779c081908171c58d345d54ae |
completed | April 23, 2026, 9:46 a.m. |
Created at: April 16, 2026, 6:25 p.m.