Triple
T18715364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Wine Route |
E457623
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Deidesheim |
—
|
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: Deidesheim | Statement: [German Wine Route, passesThrough, Deidesheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deidesheim Context triple: [German Wine Route, passesThrough, Deidesheim]
-
A.
Deidesheim
chosen
Deidesheim is a historic wine-growing town in Germany’s Rhineland-Palatinate region, renowned for its vineyards, traditional wine festivals, and picturesque old town.
-
B.
Diedelsheim
Diedelsheim is a district of the town of Bretten in the state of Baden-Württemberg in southwestern Germany.
-
C.
Ostelsheim
Ostelsheim is a small municipality in the northern Black Forest region of Baden-Württemberg in southwestern Germany.
-
D.
Dittenheim
Dittenheim is a small rural municipality in the Weißenburg-Gunzenhausen district of Bavaria in southern Germany.
-
E.
Riedisheim
Riedisheim is a commune in northeastern France’s Grand Est region, situated near the city of Mulhouse in the Haut-Rhin department.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56ab611bc819085164f252ac3a390 |
completed | April 19, 2026, 11:52 p.m. |
Created at: April 10, 2026, 11:50 a.m.