Triple
T543555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danube |
E12683
|
entity |
| Predicate | sourceRegion |
P410
|
FINISHED |
| Object | Black Forest |
E15351
|
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: Black Forest | Statement: [Danube, sourceRegion, Black Forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Black Forest Context triple: [Danube, sourceRegion, Black Forest]
-
A.
Black Forest
chosen
The Black Forest is a large, densely wooded mountain range in southwestern Germany known for its picturesque villages, cuckoo clocks, and origin of the Danube River.
-
B.
Harz
Harz is a low mountain range in central Germany known for its dense forests, mining history, and association with German folklore such as the Brocken and Walpurgis Night.
-
C.
Erzhausen
Erzhausen is a small municipality in the state of Hesse in central Germany, located near Darmstadt and part of the Rhine-Main metropolitan region.
-
D.
Hesse
Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
-
E.
Basel-Landschaft
Basel-Landschaft is a canton in northwestern Switzerland known for its proximity to Basel and its mix of industrial centers, suburban communities, and rural landscapes.
- 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_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a498dea88881908a938fe8f2313bec |
completed | March 1, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4cc5f6dfc8190a6c97a81aa8e82a3 |
completed | March 1, 2026, 11:31 p.m. |
Created at: March 1, 2026, 7:32 p.m.