Triple
T9610394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sauerland |
E232082
|
entity |
| Predicate | majorTown |
P316
|
FINISHED |
| Object | Winterberg |
E564061
|
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: Winterberg | Statement: [Sauerland, majorTown, Winterberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Winterberg Context triple: [Sauerland, majorTown, Winterberg]
-
A.
Winterberg
chosen
Winterberg is a German town in the Rothaar Mountains of North Rhine-Westphalia, known as a popular winter sports and holiday resort.
-
B.
Klingenthal
Klingenthal is a small town in the Vogtland region of Saxony, Germany, known for its long tradition of musical instrument making, especially accordions and brass instruments.
-
C.
Oberhof
Oberhof is a German winter sports town in Thuringia renowned for its biathlon, luge, and cross-country skiing facilities and World Cup events.
-
D.
Reinsberg
Reinsberg is a municipality in the German state of Saxony, located within the administrative district of Mittelsachsen.
-
E.
Seiffen
Seiffen is a village in Germany’s Ore Mountains renowned for its traditional wooden toy-making and iconic Christmas decorations.
- 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a85d4c881909ccab2e972d97e68 |
completed | April 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d179491ecc8190a72be68cc5f572b2 |
completed | April 4, 2026, 8:49 p.m. |
Created at: March 30, 2026, 8:08 p.m.