Triple
T19422386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Seseke |
E485888
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Kamen |
—
|
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: Kamen | Statement: [River Seseke, flowsThrough, Kamen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamen Context triple: [River Seseke, flowsThrough, Kamen]
-
A.
Kamen
chosen
Kamen is a town in North Rhine-Westphalia, Germany, known as a local industrial and transport hub in the Ruhr region.
-
B.
Kamen
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
-
C.
Kudan
Kudan is a historically significant site in the Greater Lumbini Area of Nepal, traditionally associated with events from the life of the Buddha.
-
D.
Kato Nevrokopi
Kato Nevrokopi is a town in northern Greece known for its harsh winters and record-low temperatures, often considered one of the coldest inhabited places in the country.
-
E.
Koyama
Koyama is a Japanese surname borne by various notable individuals across fields such as entertainment, sports, and the arts.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e632159d7081909d004544ec5992c0 |
completed | April 20, 2026, 2:03 p.m. |
Created at: April 10, 2026, 1:37 p.m.