Triple
T19058629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheinhessen wine region |
E466463
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Alzey |
—
|
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: Alzey | Statement: [Rheinhessen wine region, containsTown, Alzey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alzey Context triple: [Rheinhessen wine region, containsTown, Alzey]
-
A.
Alzey
chosen
Alzey is a historic town in the Rhineland-Palatinate region of Germany, known as one of the Nibelungen cities and for its wine-growing tradition.
-
B.
Ochsenfurt
Ochsenfurt is a historic Bavarian town in southern Germany situated on the Main River, known for its medieval architecture and wine-growing tradition.
-
C.
Aschaffenburg
Aschaffenburg is a historic Bavarian city in Germany known for its riverside setting on the Main, its prominent Schloss Johannisburg castle, and its role as a regional cultural and economic center.
-
D.
Auenheim
Auenheim is a village and district of the town of Kehl in the Ortenaukreis region of Baden-Württemberg, Germany.
-
E.
Ludwigsstadt
Ludwigsstadt is a small town in northern Bavaria, Germany, known for its location in the Franconian Forest near the Thuringian border.
- 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc08572c8190af2f8bcfe9d1616c |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 10, 2026, 12:03 p.m.