Triple
T6255905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasselwerder |
E140163
|
entity |
| Predicate | bodyOfWater |
P1778
|
FINISHED |
| Object | Lake Tegel |
E78856
|
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: Lake Tegel | Statement: [Hasselwerder, bodyOfWater, Lake Tegel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Tegel Context triple: [Hasselwerder, bodyOfWater, Lake Tegel]
-
A.
Lake Tegel
chosen
Lake Tegel is a large lake in the northwest of Berlin, Germany, known for its recreational areas, beaches, and surrounding forests.
-
B.
Stadtsee
Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
-
C.
Lake Heiligensee
Lake Heiligensee is a small freshwater lake in the Heiligensee district of Berlin, Germany, known for its recreational use and scenic natural surroundings.
-
D.
Goitzsche lake
Goitzsche lake is a large artificial lake in Saxony-Anhalt, Germany, created by flooding former open-cast lignite mines and now used as a recreational and nature area.
-
E.
Lake Seliger
Lake Seliger is a large glacial lake in central Russia known for its scenic islands, pine forests, and popularity as a nature and recreation destination.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063653910819095f1dc3b90ce77db |
completed | March 22, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c63859155881908767074ea315198c |
completed | March 27, 2026, 7:57 a.m. |
Created at: March 22, 2026, 4:24 p.m.