Triple
T13754640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donau-Ries |
E330443
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Münster (Lech)
Münster (Lech) is a small municipality in the Donau-Ries district of Bavaria, Germany, situated along the Lech River.
|
E1060511
|
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: Münster (Lech) | Statement: [Donau-Ries, containsMunicipality, Münster (Lech)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Münster (Lech) Context triple: [Donau-Ries, containsMunicipality, Münster (Lech)]
-
A.
Münsing
Münsing is a municipality in Bavaria, Germany, located near Lake Starnberg and known for its scenic rural landscape and proximity to the Alps.
-
B.
Münnich
Münnich is a German-language surname borne by various notable individuals, including Hungarian communist politician Ferenc Münnich.
-
C.
Eschbach
Eschbach is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Seewiesen
Seewiesen is a research locality in Bavaria, Germany, best known for its ornithological and behavioral science institutes associated with Konrad Lorenz and other pioneering ethologists.
-
E.
Maroldsweisach
Maroldsweisach is a municipality in the Haßberge district of northern Bavaria, Germany, known for its rural setting and historic Franconian character.
- 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: Münster (Lech) Triple: [Donau-Ries, containsMunicipality, Münster (Lech)]
Generated description
Münster (Lech) is a small municipality in the Donau-Ries district of Bavaria, Germany, situated along the Lech River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Münster (Lech) Target entity description: Münster (Lech) is a small municipality in the Donau-Ries district of Bavaria, Germany, situated along the Lech River.
-
A.
Münsing
Münsing is a municipality in Bavaria, Germany, located near Lake Starnberg and known for its scenic rural landscape and proximity to the Alps.
-
B.
Münnich
Münnich is a German-language surname borne by various notable individuals, including Hungarian communist politician Ferenc Münnich.
-
C.
Eschbach
Eschbach is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Seewiesen
Seewiesen is a research locality in Bavaria, Germany, best known for its ornithological and behavioral science institutes associated with Konrad Lorenz and other pioneering ethologists.
-
E.
Maroldsweisach
Maroldsweisach is a municipality in the Haßberge district of northern Bavaria, Germany, known for its rural setting and historic Franconian character.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02179c948190a652cc8c586e418f |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a859e6748190aa1899830a02b710 |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a91deb3c8190ad2be7f1ca99ac9b |
completed | May 3, 2026, 7:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7ad51c6808190afa80fc3622399bf |
completed | May 3, 2026, 8:17 p.m. |
Created at: April 9, 2026, 10:09 p.m.