Triple
T13972603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Müller-Thurgau |
E336101
|
entity |
| Predicate | regionOfOrigin |
P410
|
FINISHED |
| Object | Geisenheim |
E809161
|
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: Geisenheim | Statement: [Müller-Thurgau, regionOfOrigin, Geisenheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geisenheim Context triple: [Müller-Thurgau, regionOfOrigin, Geisenheim]
-
A.
Geisenheim
chosen
Geisenheim is a German town in the Rheingau wine region, known for its viticulture, wine production, and renowned university of applied sciences for wine and horticulture.
-
B.
Passenheim
Passenheim is the former German name of the town now known as Pasym, located in northeastern Poland’s historic region of Masuria.
-
C.
Wiehl
Wiehl is a small town in western Germany’s North Rhine-Westphalia region, known for its picturesque setting in the hilly Bergisches Land and its mix of rural charm and light industry.
-
D.
Mülhausen
Mülhausen is the German name for the city of Mulhouse, a historically industrial and culturally significant city in the Alsace region of present-day France.
-
E.
Heroldsberg
Heroldsberg is a municipality in the Erlangen-Höchstadt district of Bavaria, Germany, known for its historic center and proximity to the city of Nuremberg.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8eae40819080dd4bd25c73b6d6 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd323e89948190bb280e93e2058c0a |
completed | May 8, 2026, 12:45 a.m. |
Created at: April 9, 2026, 10:18 p.m.