Triple
T9146460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münsing |
E219467
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Weipertshausen
Weipertshausen is a small locality that forms part of the municipality of Münsing in Bavaria, Germany.
|
E830795
|
NE FINISHED |
How this triple was built (4 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: Weipertshausen | Statement: [Münsing, hasSubdivision, Weipertshausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weipertshausen Context triple: [Münsing, hasSubdivision, Weipertshausen]
-
A.
Lamprechtshausen
Lamprechtshausen is a municipality in the Austrian state of Salzburg, known for its rural character and location within the Salzburg-Umgebung region.
-
B.
Waigolshausen
Waigolshausen is a small municipality in the Schweinfurt district of Bavaria, Germany, known for its rural character and location in the Franconian region.
-
C.
Deisenhausen
Deisenhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
-
D.
Assmannshausen
Assmannshausen is a renowned wine-producing village in Germany’s Rheingau region, particularly famous for its red wines made from Spätburgunder (Pinot Noir).
-
E.
Niedernhausen
Niedernhausen is a municipality in the Rheingau-Taunus district of Hesse, Germany, known for its wooded surroundings in the Taunus hills and convenient rail and road links to Wiesbaden and Frankfurt.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Weipertshausen Triple: [Münsing, hasSubdivision, Weipertshausen]
Generated description
Weipertshausen is a small locality that forms part of the municipality of Münsing in Bavaria, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Weipertshausen Target entity description: Weipertshausen is a small locality that forms part of the municipality of Münsing in Bavaria, Germany.
-
A.
Lamprechtshausen
Lamprechtshausen is a municipality in the Austrian state of Salzburg, known for its rural character and location within the Salzburg-Umgebung region.
-
B.
Waigolshausen
Waigolshausen is a small municipality in the Schweinfurt district of Bavaria, Germany, known for its rural character and location in the Franconian region.
-
C.
Deisenhausen
Deisenhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
-
D.
Assmannshausen
Assmannshausen is a renowned wine-producing village in Germany’s Rheingau region, particularly famous for its red wines made from Spätburgunder (Pinot Noir).
-
E.
Niedernhausen
Niedernhausen is a municipality in the Rheingau-Taunus district of Hesse, Germany, known for its wooded surroundings in the Taunus hills and convenient rail and road links to Wiesbaden and Frankfurt.
- F. None of above. chosen
Provenance (5 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_69ca83e121dc81909912bd66953081c5 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca917914c8190b97ca9169bbd1e5e |
completed | April 1, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228318de881909bbf4e68331bb586 |
completed | April 5, 2026, 9:15 a.m. |
| NEDg | Description generation | batch_69d22968194c8190b919ed3ab2dcfc33 |
completed | April 5, 2026, 9:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d229d10dac81909f34a9977e6445a0 |
completed | April 5, 2026, 9:22 a.m. |
Created at: March 30, 2026, 7:20 p.m.