Triple
T6255923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasselwerder |
E140163
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Hasselwerder (German) |
E140163
|
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: Hasselwerder (German) | Statement: [Hasselwerder, hasNameInLanguage, Hasselwerder (German)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hasselwerder (German) Context triple: [Hasselwerder, hasNameInLanguage, Hasselwerder (German)]
-
A.
Hasselwerder
chosen
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
B.
Petershagen
Petershagen is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and scenic location along the Weser River.
-
C.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
D.
Sprockhövel
Sprockhövel is a small town in North Rhine-Westphalia, Germany, known for its historical coal mining heritage and location in the hilly Ruhr region.
-
E.
Hakenfelde
Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
- 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_69c244379f308190b73fe7ed4ed678e9 |
completed | March 24, 2026, 7:58 a.m. |
Created at: March 22, 2026, 4:24 p.m.