Triple
T9262803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Köterberg |
E222618
|
entity |
| Predicate | offersViewOf |
P3821
|
FINISHED |
| Object | Solling |
E229268
|
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: Solling | Statement: [Köterberg, offersViewOf, Solling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Solling Context triple: [Köterberg, offersViewOf, Solling]
-
A.
Solling
chosen
Solling is a forested low mountain range in Lower Saxony, Germany, known for its extensive woodlands and role as a major part of the Weser Uplands.
-
B.
Radeberg
Radeberg is a small town in the German state of Saxony, known for its Radeberger Pilsner brewery and historic town center near Dresden.
-
C.
Kellerwald
Kellerwald is a low mountain forest region in central Germany known for its ancient beech woodlands and protected national park status.
-
D.
Bernlohe
Bernlohe is a village-level district that forms part of the town of Roth in Bavaria, Germany.
-
E.
Eschwege
Eschwege is a small historic town in the German state of Hesse, known for its medieval architecture and location near the Werra River.
- 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_69ca841f2e808190a64f4c31903a1332 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07189bc48190987315bd5d3ce4f1 |
completed | April 1, 2026, 11:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09c0526c48190aae93a70cfe5562c |
completed | April 4, 2026, 5:05 a.m. |
Created at: March 30, 2026, 7:32 p.m.