Triple
T13113885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joachimsthal |
E311042
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Werbellinsee
Werbellinsee is a large, scenic glacial lake in Brandenburg, Germany, popular for recreation and nature tourism.
|
E1044925
|
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: Werbellinsee | Statement: [Joachimsthal, hasLake, Werbellinsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werbellinsee Context triple: [Joachimsthal, hasLake, Werbellinsee]
-
A.
Fälensee
Fälensee is a picturesque alpine lake in the Alpstein massif of northeastern Switzerland, popular for hiking and mountain scenery.
-
B.
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.
-
C.
Egelsee
Egelsee is a locality within the Austrian city of Krems an der Donau, known for its residential character and proximity to the Wachau cultural landscape.
-
D.
Ziegelsee
Ziegelsee is a lake in the city of Schwerin in northern Germany, known for its scenic waterfront and role in the region’s interconnected lake system.
-
E.
Fleesensee
Fleesensee is a large lake in northeastern Germany known for its popular holiday resorts, water sports, and scenic natural surroundings.
- 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: Werbellinsee Triple: [Joachimsthal, hasLake, Werbellinsee]
Generated description
Werbellinsee is a large, scenic glacial lake in Brandenburg, Germany, popular for recreation and nature tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Werbellinsee Target entity description: Werbellinsee is a large, scenic glacial lake in Brandenburg, Germany, popular for recreation and nature tourism.
-
A.
Fälensee
Fälensee is a picturesque alpine lake in the Alpstein massif of northeastern Switzerland, popular for hiking and mountain scenery.
-
B.
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.
-
C.
Egelsee
Egelsee is a locality within the Austrian city of Krems an der Donau, known for its residential character and proximity to the Wachau cultural landscape.
-
D.
Ziegelsee
Ziegelsee is a lake in the city of Schwerin in northern Germany, known for its scenic waterfront and role in the region’s interconnected lake system.
-
E.
Fleesensee
Fleesensee is a large lake in northeastern Germany known for its popular holiday resorts, water sports, and scenic natural surroundings.
- 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_69f75469a3f08190a7e417872147b455 |
completed | May 3, 2026, 1:58 p.m. |
| NEDg | Description generation | batch_69f755d330c481909c159d3801c18f59 |
completed | May 3, 2026, 2:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f756773b9c81908250ae7ffc2d8d99 |
completed | May 3, 2026, 2:06 p.m. |
Created at: April 9, 2026, 9:06 p.m.