Triple
T14445989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Havel |
E358205
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Wannsee |
E4533
|
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: Wannsee | Statement: [River Havel, connectedTo, Wannsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wannsee Context triple: [River Havel, connectedTo, Wannsee]
-
A.
Wannsee
chosen
Wannsee is a lakeside district in southwestern Berlin, Germany, known for its villa colonies, recreational waterfront, and as the site of the infamous 1942 Wannsee Conference.
-
B.
Kaufering
Kaufering is a municipality in Bavaria, Germany, known historically for its World War II subcamps of Dachau and its location near the town of Landsberg am Lech.
-
C.
Pulhof
Pulhof is a residential neighborhood in the Antwerp district of Berchem, Belgium, known for its quiet streets and urban character.
-
D.
Neubukow
Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
-
E.
Kasendorf
Kasendorf is a small municipality in the Upper Franconia region of Bavaria, Germany, known for its rural character and scenic surroundings.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de915e76f481909fe9462f964b5b1c |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bdd0f388190870ddd01f66d3e99 |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:19 a.m.