Triple
T10796019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miesbach district |
E254707
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bayrischzell
Bayrischzell is a picturesque alpine village and ski resort in Upper Bavaria, Germany, known for its mountain scenery and outdoor recreation.
|
E913730
|
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: Bayrischzell | Statement: [Miesbach district, contains, Bayrischzell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayrischzell Context triple: [Miesbach district, contains, Bayrischzell]
-
A.
Eberhardzell
Eberhardzell is a rural municipality in the district of Biberach in the German state of Baden-Württemberg.
-
B.
Kirchlindach
Kirchlindach is a Swiss municipality in the canton of Bern, known for its rural character and proximity to the city of Bern.
-
C.
Zell am Harmersbach
Zell am Harmersbach is a small historic town in the Black Forest region of southwestern Germany, known for its picturesque old town and traditional half-timbered houses.
-
D.
Steinlach
Steinlach is a small river in the German state of Baden-Württemberg that flows through the city of Tübingen before joining the Neckar.
-
E.
Waltershof
Waltershof is an industrial and port district of Hamburg, Germany, located within the borough of Hamburg-Mitte.
- 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: Bayrischzell Triple: [Miesbach district, contains, Bayrischzell]
Generated description
Bayrischzell is a picturesque alpine village and ski resort in Upper Bavaria, Germany, known for its mountain scenery and outdoor recreation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bayrischzell Target entity description: Bayrischzell is a picturesque alpine village and ski resort in Upper Bavaria, Germany, known for its mountain scenery and outdoor recreation.
-
A.
Eberhardzell
Eberhardzell is a rural municipality in the district of Biberach in the German state of Baden-Württemberg.
-
B.
Kirchlindach
Kirchlindach is a Swiss municipality in the canton of Bern, known for its rural character and proximity to the city of Bern.
-
C.
Zell am Harmersbach
Zell am Harmersbach is a small historic town in the Black Forest region of southwestern Germany, known for its picturesque old town and traditional half-timbered houses.
-
D.
Steinlach
Steinlach is a small river in the German state of Baden-Württemberg that flows through the city of Tübingen before joining the Neckar.
-
E.
Waltershof
Waltershof is an industrial and port district of Hamburg, Germany, located within the borough of Hamburg-Mitte.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73332dbfc8190904434846957b618 |
completed | April 9, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4cbce653481909b201a2d5871e129 |
completed | April 19, 2026, 12:34 p.m. |
| NEDg | Description generation | batch_69e4d9e87508819080932fac06fb754d |
completed | April 19, 2026, 1:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4dda28b0081909245b65faae3533b |
completed | April 19, 2026, 1:50 p.m. |
Created at: April 8, 2026, 9:17 p.m.