Triple
T10130000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markkleeberg |
E226311
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object | Cospudener See |
E738198
|
NE FINISHED |
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: Cospudener See | Statement: [Markkleeberg, hasLake, Cospudener See]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cospudener See Context triple: [Markkleeberg, hasLake, Cospudener See]
-
A.
Cospudener See
chosen
Cospudener See is a popular artificial lake and recreational area near Leipzig in Saxony, Germany, known for swimming, sailing, and lakeside leisure activities.
-
B.
Spullersee
Spullersee is a high-altitude reservoir lake in the Austrian Alps, known for its scenic mountain setting and use in regional hydroelectric power generation.
-
C.
Waginger See
Waginger See is a warm, scenic lake in southeastern Bavaria, Germany, popular for swimming, water sports, and tourism in the Chiemgau region.
-
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.
Grunewaldsee
Grunewaldsee is a popular forest lake in Berlin known for its scenic surroundings and dog-friendly bathing areas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca843057b48190a86730167f5d6b98 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd33438988190be45878f98695816 |
completed | April 2, 2026, 2:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4415b7f848190a9fc8b08824f0b9b |
completed | April 19, 2026, 2:43 a.m. |
Created at: March 30, 2026, 9:05 p.m.