Triple
T13113805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wandlitz |
E311040
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Klosterfelde
Klosterfelde is a village and district of the municipality of Wandlitz in the state of Brandenburg, Germany.
|
E1021577
|
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: Klosterfelde | Statement: [Wandlitz, hasSubdivision, Klosterfelde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klosterfelde Context triple: [Wandlitz, hasSubdivision, Klosterfelde]
-
A.
Fürstenzell
Fürstenzell is a market town and municipality in Lower Bavaria, Germany, known for its historic monastery and rural setting near the city of Passau.
-
B.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
C.
Fürstenried
Fürstenried is a residential district in the southwest of Munich, Germany, known for its post-war housing estates and proximity to green spaces and the Fürstenried Palace.
-
D.
Gröbenzell
Gröbenzell is a suburban town in Upper Bavaria, Germany, known for its residential character and proximity to Munich.
-
E.
Wallenfels
Wallenfels is a small town in northern Bavaria, Germany, known for its scenic location in the Franconian Forest region.
- 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: Klosterfelde Triple: [Wandlitz, hasSubdivision, Klosterfelde]
Generated description
Klosterfelde is a village and district of the municipality of Wandlitz in the state of Brandenburg, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Klosterfelde Target entity description: Klosterfelde is a village and district of the municipality of Wandlitz in the state of Brandenburg, Germany.
-
A.
Fürstenzell
Fürstenzell is a market town and municipality in Lower Bavaria, Germany, known for its historic monastery and rural setting near the city of Passau.
-
B.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
C.
Fürstenried
Fürstenried is a residential district in the southwest of Munich, Germany, known for its post-war housing estates and proximity to green spaces and the Fürstenried Palace.
-
D.
Gröbenzell
Gröbenzell is a suburban town in Upper Bavaria, Germany, known for its residential character and proximity to Munich.
-
E.
Wallenfels
Wallenfels is a small town in northern Bavaria, Germany, known for its scenic location in the Franconian Forest region.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9817f8ee8819084078b4bec5e4f18 |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e27f5c4481909bc323c9d0c83dc9 |
completed | May 3, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69f6e32bf5508190b4dc58971f8f64d0 |
completed | May 3, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e407dd988190b928b8931985a815 |
completed | May 3, 2026, 5:58 a.m. |
Created at: April 9, 2026, 9:06 p.m.