Triple
T20284343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Müritz |
E509836
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Fleesensee |
—
|
NE NERFINISHED |
How this triple was built (2 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: Fleesensee | Statement: [Müritz, connectedTo, Fleesensee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fleesensee Context triple: [Müritz, connectedTo, Fleesensee]
-
A.
Fleesensee
chosen
Fleesensee is a large lake in northeastern Germany known for its popular holiday resorts, water sports, and scenic natural surroundings.
-
B.
Fälensee
Fälensee is a picturesque alpine lake in the Alpstein massif of northeastern Switzerland, popular for hiking and mountain scenery.
-
C.
Fennsee
Fennsee is a small urban lake and surrounding green area in Berlin’s Wilmersdorf district, popular for local recreation and walking.
-
D.
Wendsee
Wendsee is a lake in the German state of Brandenburg that forms part of the interconnected waterway system near Plauer See.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c652388190b782cad965e5a098 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e67691516c81909f32b176edb6214c |
completed | April 20, 2026, 6:55 p.m. |
Created at: April 16, 2026, 10:56 a.m.