Triple
T19565494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | district of Lörrach |
E489570
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Malsburg-Marzell
Malsburg-Marzell is a small municipality in southwestern Germany’s Baden-Württemberg region, known for its scenic location in the Black Forest near the Swiss and French borders.
|
E1383163
|
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: Malsburg-Marzell | Statement: [district of Lörrach, containsMunicipality, Malsburg-Marzell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malsburg-Marzell Context triple: [district of Lörrach, containsMunicipality, Malsburg-Marzell]
-
A.
Malsfeld
Malsfeld is a small municipality in central Germany known for its rural character and location near the Fulda River.
-
B.
Mengerhausen
Mengerhausen is a locality in Germany known primarily as the place where the screenwriter Franz Schulz died.
-
C.
Calmbach
Calmbach is a small town in Germany’s Black Forest region, known for its scenic location in the Enz Valley and traditional spa and nature tourism.
-
D.
Malgersdorf
Malgersdorf is a small municipality in the Rottal-Inn district of Lower Bavaria, Germany.
-
E.
Münchberg
Münchberg is a town in the Upper Franconia region of Bavaria, Germany, known historically for its textile industry and as a local commercial center.
- 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: Malsburg-Marzell Triple: [district of Lörrach, containsMunicipality, Malsburg-Marzell]
Generated description
Malsburg-Marzell is a small municipality in southwestern Germany’s Baden-Württemberg region, known for its scenic location in the Black Forest near the Swiss and French borders.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Malsburg-Marzell Target entity description: Malsburg-Marzell is a small municipality in southwestern Germany’s Baden-Württemberg region, known for its scenic location in the Black Forest near the Swiss and French borders.
-
A.
Malsfeld
Malsfeld is a small municipality in central Germany known for its rural character and location near the Fulda River.
-
B.
Mengerhausen
Mengerhausen is a locality in Germany known primarily as the place where the screenwriter Franz Schulz died.
-
C.
Calmbach
Calmbach is a small town in Germany’s Black Forest region, known for its scenic location in the Enz Valley and traditional spa and nature tourism.
-
D.
Malgersdorf
Malgersdorf is a small municipality in the Rottal-Inn district of Lower Bavaria, Germany.
-
E.
Münchberg
Münchberg is a town in the Upper Franconia region of Bavaria, Germany, known historically for its textile industry and as a local commercial center.
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63f777cf081909312b46ac09bce7c |
completed | April 20, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a075787d15c8190b680c74afa7b2220 |
completed | May 15, 2026, 5:27 p.m. |
| NEDg | Description generation | batch_6a0758aeffb081909c100c7509010d54 |
completed | May 15, 2026, 5:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07598fdd908190b3f999b61447f8d2 |
completed | May 15, 2026, 5:36 p.m. |
Created at: April 10, 2026, 1:42 p.m.