Triple
T17820890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langer See |
E444977
|
entity |
| Predicate | hasShoreOn |
P26435
|
FINISHED |
| Object | Grünau |
—
|
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: Grünau | Statement: [Langer See, hasShoreOn, Grünau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grünau Context triple: [Langer See, hasShoreOn, Grünau]
-
A.
Grünau
chosen
Grünau is a waterside locality in Berlin known for its lakeside recreation areas, rowing facilities, and green residential surroundings.
-
B.
Grünau
Grünau is a small rural town in southern Namibia, situated in the arid Karas Region near key transport routes linking the country to South Africa.
-
C.
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.
-
D.
Ruppichteroth
Ruppichteroth is a small municipality in western Germany’s North Rhine-Westphalia region, characterized by its rural setting and proximity to the metropolitan area of Cologne-Bonn.
-
E.
Kunreuth
Kunreuth is a small municipality in the Upper Franconia region of Bavaria, Germany, known for its rural character and historic castle.
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48910eb8881908db8ec08e2752d7d |
completed | April 19, 2026, 7:49 a.m. |
Created at: April 10, 2026, 10:15 a.m.