Triple
T3686005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spandau |
E78225
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Hakenfelde
Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
|
E388661
|
NE FINISHED |
How this triple was built (4 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: Hakenfelde | Statement: [Spandau, hasSubdivision, Hakenfelde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hakenfelde Context triple: [Spandau, hasSubdivision, Hakenfelde]
-
A.
Hasselfelde
Hasselfelde is a small town in the Harz region of central Germany, now incorporated into the municipality of Oberharz am Brocken.
-
B.
Hennigsdorf
Hennigsdorf is a town in the German state of Brandenburg, located just northwest of Berlin and known for its industrial heritage and proximity to the Havel River.
-
C.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
D.
Friedrichsruh
Friedrichsruh is a small village in northern Germany best known as the estate and final residence of statesman Otto von Bismarck.
-
E.
Göhren
Göhren is a seaside resort town on the Baltic Sea coast of Germany, located on the island of Rügen and known for its beaches and tourism.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hakenfelde Triple: [Spandau, hasSubdivision, Hakenfelde]
Generated description
Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hakenfelde Target entity description: Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
-
A.
Hasselfelde
Hasselfelde is a small town in the Harz region of central Germany, now incorporated into the municipality of Oberharz am Brocken.
-
B.
Hennigsdorf
Hennigsdorf is a town in the German state of Brandenburg, located just northwest of Berlin and known for its industrial heritage and proximity to the Havel River.
-
C.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
D.
Friedrichsruh
Friedrichsruh is a small village in northern Germany best known as the estate and final residence of statesman Otto von Bismarck.
-
E.
Göhren
Göhren is a seaside resort town on the Baltic Sea coast of Germany, located on the island of Rügen and known for its beaches and tourism.
- F. None of above. chosen
Provenance (5 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c676748190b074abfb9ba43b49 |
completed | March 8, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f021fe148190af7ba4b36caa0ce2 |
completed | March 14, 2026, 5:20 a.m. |
| NEDg | Description generation | batch_69b4f1403de08190b31120a384f08cbb |
completed | March 14, 2026, 5:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4f1fc2e248190a966cace3cc69449 |
completed | March 14, 2026, 5:28 a.m. |
Created at: March 8, 2026, 3:26 p.m.