Triple
T12517531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stölpchensee |
E299226
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Grunewald forest |
E25888
|
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: Grunewald forest | Statement: [Stölpchensee, locatedIn, Grunewald forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grunewald forest Context triple: [Stölpchensee, locatedIn, Grunewald forest]
-
A.
Grunewald forest
chosen
Grunewald forest is a large woodland and recreational area in western Berlin, known for its lakes, walking trails, and natural landscapes.
-
B.
Wermsdorf Forest
Wermsdorf Forest is a large woodland area in Saxony, Germany, known for its historic hunting grounds and scenic landscapes.
-
C.
Schorfheide forest
Schorfheide forest is a large historic woodland and former royal hunting reserve in Brandenburg, Germany, known for its rich biodiversity and use as a retreat by political leaders.
-
D.
Granitz Forest
Granitz Forest is a scenic woodland area on Germany’s Rügen Island, known for its beech forests, hiking trails, and the historic Granitz Hunting Lodge.
-
E.
Göttingen Forest
Göttingen Forest is a wooded hill range in Lower Saxony, Germany, known for its extensive hiking trails and proximity to the university city of Göttingen.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9541f80148190976d1d912fe155d0 |
completed | April 10, 2026, 7:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bbd58b88190baeb99380babf64f |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:57 p.m.