Triple
T16039999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angermünde |
E389067
|
entity |
| Predicate | hasNearbyLake |
P17985
|
FINISHED |
| Object |
Mündesee
Mündesee is a lake in northeastern Germany located near the town of Angermünde in the state of Brandenburg.
|
E1190733
|
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: Mündesee | Statement: [Angermünde, hasNearbyLake, Mündesee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mündesee Context triple: [Angermünde, hasNearbyLake, Mündesee]
-
A.
Fälensee
Fälensee is a picturesque alpine lake in the Alpstein massif of northeastern Switzerland, popular for hiking and mountain scenery.
-
B.
Möhnesee
Möhnesee is a municipality in North Rhine-Westphalia, Germany, known for its large reservoir and scenic recreational area around the Möhne River.
-
C.
Dämeritzsee
Dämeritzsee is a lake on the southeastern edge of Berlin, Germany, known as a popular recreational area and a key junction in the region’s interconnected waterways.
-
D.
Weissensee
Weissensee is a picturesque alpine lake and surrounding region in southern Austria, renowned for its clear waters, outdoor recreation, and unspoiled natural landscape.
-
E.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
- 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: Mündesee Triple: [Angermünde, hasNearbyLake, Mündesee]
Generated description
Mündesee is a lake in northeastern Germany located near the town of Angermünde in the state of Brandenburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mündesee Target entity description: Mündesee is a lake in northeastern Germany located near the town of Angermünde in the state of Brandenburg.
-
A.
Fälensee
Fälensee is a picturesque alpine lake in the Alpstein massif of northeastern Switzerland, popular for hiking and mountain scenery.
-
B.
Möhnesee
Möhnesee is a municipality in North Rhine-Westphalia, Germany, known for its large reservoir and scenic recreational area around the Möhne River.
-
C.
Dämeritzsee
Dämeritzsee is a lake on the southeastern edge of Berlin, Germany, known as a popular recreational area and a key junction in the region’s interconnected waterways.
-
D.
Weissensee
Weissensee is a picturesque alpine lake and surrounding region in southern Austria, renowned for its clear waters, outdoor recreation, and unspoiled natural landscape.
-
E.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1833f84188190baa3a452df880284 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbd77c5481908644742a8a8f3e68 |
completed | May 10, 2026, 1:13 a.m. |
| NEDg | Description generation | batch_69ffdc4f3d488190a5d1bffd4432ac32 |
completed | May 10, 2026, 1:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffdd0392e08190af42a0cdc5dd4c1f |
completed | May 10, 2026, 1:18 a.m. |
Created at: April 10, 2026, 4:56 a.m.