Triple
T19992413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marktbreit |
E494094
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Kitzingen |
—
|
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: Kitzingen | Statement: [Marktbreit, locatedNear, Kitzingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kitzingen Context triple: [Marktbreit, locatedNear, Kitzingen]
-
A.
Kitzingen
chosen
Kitzingen is a historic town in northern Bavaria, Germany, known for its wine production and location along the Main River.
-
B.
Günzburg
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
-
C.
Wolfratshausen
Wolfratshausen is a Bavarian town in southern Germany known for its historic old town, riverside setting on the Loisach and Isar, and proximity to Munich and the Alps.
-
D.
Ludwigsstadt
Ludwigsstadt is a small town in northern Bavaria, Germany, known for its location in the Franconian Forest near the Thuringian border.
-
E.
Kulmbach
Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fe10ffc81908c94168b0a8ea9c9 |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:31 p.m.