Triple
T15687455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fürstenberg/Havel |
E380238
|
entity |
| Predicate | locatedByLake |
P17985
|
FINISHED |
| Object |
Baalensee
Baalensee is a lake in northeastern Germany, situated near the town of Fürstenberg/Havel in the state of Brandenburg.
|
E1181204
|
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: Baalensee | Statement: [Fürstenberg/Havel, locatedByLake, Baalensee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baalensee Context triple: [Fürstenberg/Havel, locatedByLake, Baalensee]
-
A.
Scharmützelsee
Scharmützelsee is a popular lake in eastern Germany known for its scenic surroundings, recreational activities, and spa resorts.
-
B.
Teufelssee
Teufelssee is a small natural lake in Berlin known for its scenic setting, recreational swimming, and clothing-optional bathing area.
-
C.
Grüntensee
Grüntensee is a scenic lake in the Ostallgäu region of Bavaria, Germany, popular for recreation and surrounded by Alpine landscapes.
-
D.
Möhnesee
Möhnesee is a municipality in North Rhine-Westphalia, Germany, known for its large reservoir and scenic recreational area around the Möhne River.
-
E.
Großer Wannsee lake
Großer Wannsee lake is a popular recreational lake in southwestern Berlin, known for its beaches, sailing, and proximity to historically significant sites.
- 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: Baalensee Triple: [Fürstenberg/Havel, locatedByLake, Baalensee]
Generated description
Baalensee is a lake in northeastern Germany, situated near the town of Fürstenberg/Havel in the state of Brandenburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Baalensee Target entity description: Baalensee is a lake in northeastern Germany, situated near the town of Fürstenberg/Havel in the state of Brandenburg.
-
A.
Scharmützelsee
Scharmützelsee is a popular lake in eastern Germany known for its scenic surroundings, recreational activities, and spa resorts.
-
B.
Teufelssee
Teufelssee is a small natural lake in Berlin known for its scenic setting, recreational swimming, and clothing-optional bathing area.
-
C.
Grüntensee
Grüntensee is a scenic lake in the Ostallgäu region of Bavaria, Germany, popular for recreation and surrounded by Alpine landscapes.
-
D.
Möhnesee
Möhnesee is a municipality in North Rhine-Westphalia, Germany, known for its large reservoir and scenic recreational area around the Möhne River.
-
E.
Großer Wannsee lake
Großer Wannsee lake is a popular recreational lake in southwestern Berlin, known for its beaches, sailing, and proximity to historically significant sites.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f4cee5481908699fbb2b7bdd2f6 |
completed | April 16, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa9341a0c81909057dc338f218b85 |
completed | May 9, 2026, 9:37 p.m. |
| NEDg | Description generation | batch_69ffaa3903408190b7beaa6b461bd2bd |
completed | May 9, 2026, 9:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffab0c79d4819085f0ed6a4edcb7fb |
completed | May 9, 2026, 9:45 p.m. |
Created at: April 10, 2026, 4:44 a.m.