Triple
T11133300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unstrut |
E263342
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Mühlhausen |
E351668
|
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: Mühlhausen | Statement: [Unstrut, flowsThrough, Mühlhausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mühlhausen Context triple: [Unstrut, flowsThrough, Mühlhausen]
-
A.
Mühlhausen
chosen
Mühlhausen is a historic town in central Germany, known for its well-preserved medieval architecture and cultural heritage.
-
B.
Maichingen
Maichingen is a district of the city of Sindelfingen in the German state of Baden-Württemberg.
-
C.
Ebermannstadt
Ebermannstadt is a small historic town in northern Bavaria, Germany, known as a gateway to the scenic Franconian Switzerland region.
-
D.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
-
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 (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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8347a248190837e8c26f25f553a |
completed | April 9, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a0e90e48190af6b802697d3256f |
completed | May 3, 2026, 8:40 a.m. |
Created at: April 8, 2026, 9:28 p.m.