Triple
T23014839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grunewald |
E573002
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object | Grunewaldsee |
—
|
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: Grunewaldsee | Statement: [Grunewald, hasLake, Grunewaldsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grunewaldsee Context triple: [Grunewald, hasLake, Grunewaldsee]
-
A.
Grunewaldsee
chosen
Grunewaldsee is a popular forest lake in Berlin known for its scenic surroundings and dog-friendly bathing areas.
-
B.
Rothsee
Rothsee is an artificial recreational lake in Middle Franconia, Bavaria, popular for swimming, sailing, and other water sports.
-
C.
Stutensee
Stutensee is a town in the district of Karlsruhe in the state of Baden-Württemberg in southwestern Germany.
-
D.
Cospudener See
Cospudener See is a popular artificial lake and recreational area near Leipzig in Saxony, Germany, known for swimming, sailing, and lakeside leisure activities.
-
E.
Weissensee
Weissensee is a picturesque alpine lake and surrounding region in southern Austria, renowned for its clear waters, outdoor recreation, and unspoiled natural 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e3c0e08190a7ac747b056ec3ca |
completed | April 29, 2026, 4:06 a.m. |
Created at: April 17, 2026, 3:51 p.m.