Triple
T21166682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rotenburg an der Fulda |
E521581
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Mündershausen
Mündershausen is a small village and district of the town Rotenburg an der Fulda in the state of Hesse, Germany.
|
E1471808
|
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: Mündershausen | Statement: [Rotenburg an der Fulda, hasSubdivision, Mündershausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mündershausen Context triple: [Rotenburg an der Fulda, hasSubdivision, Mündershausen]
-
A.
Nörtershausen
Nörtershausen is a small municipality in western Germany’s Rhineland-Palatinate region, situated in the Rhine-Mosel area.
-
B.
Willingshausen
Willingshausen is a small municipality in central Germany known for its historic artists’ colony and rural cultural heritage.
-
C.
Mörfelden
Mörfelden is a district of the town Mörfelden-Walldorf in Hesse, Germany, known for its proximity to Frankfurt Airport and its mix of residential and industrial areas.
-
D.
Rüdinghausen
Rüdinghausen is a district of the city of Witten in North Rhine-Westphalia, Germany, characterized by its residential areas and local amenities.
-
E.
Nennhausen
Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
- 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: Mündershausen Triple: [Rotenburg an der Fulda, hasSubdivision, Mündershausen]
Generated description
Mündershausen is a small village and district of the town Rotenburg an der Fulda in the state of Hesse, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mündershausen Target entity description: Mündershausen is a small village and district of the town Rotenburg an der Fulda in the state of Hesse, Germany.
-
A.
Nörtershausen
Nörtershausen is a small municipality in western Germany’s Rhineland-Palatinate region, situated in the Rhine-Mosel area.
-
B.
Willingshausen
Willingshausen is a small municipality in central Germany known for its historic artists’ colony and rural cultural heritage.
-
C.
Mörfelden
Mörfelden is a district of the town Mörfelden-Walldorf in Hesse, Germany, known for its proximity to Frankfurt Airport and its mix of residential and industrial areas.
-
D.
Rüdinghausen
Rüdinghausen is a district of the city of Witten in North Rhine-Westphalia, Germany, characterized by its residential areas and local amenities.
-
E.
Nennhausen
Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
- 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_69e0b50e30748190b186824a206d39b9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7270fce708190a5715b55b406dd4e |
completed | April 21, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a097ec096f081909c17403ffd612db7 |
completed | May 17, 2026, 8:39 a.m. |
| NEDg | Description generation | batch_6a098022524481908858de1c80172973 |
completed | May 17, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09810158948190a9504d50c964efd9 |
completed | May 17, 2026, 8:49 a.m. |
Created at: April 16, 2026, 2:59 p.m.