Triple
T3842364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pankow |
E93479
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Blankenfelde
Blankenfelde is a locality within the Berlin borough of Pankow, known for its residential character and proximity to green spaces.
|
E393553
|
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: Blankenfelde | Statement: [Pankow, contains, Blankenfelde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blankenfelde Context triple: [Pankow, contains, Blankenfelde]
-
A.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
B.
Boven Pekela
Boven Pekela is a village in the municipality of Pekela in the province of Groningen in the northeastern Netherlands.
-
C.
Brackenheim
Brackenheim is a small town in the German state of Baden-Württemberg, best known as the birthplace of Theodor Heuss, the first President of the Federal Republic of Germany.
-
D.
Falkeplatz
Falkeplatz is a location in Chemnitz, Germany, known for hosting cultural institutions such as the Museum Gunzenhauser.
-
E.
Havelterberg
Havelterberg is a modest hill and natural area in the Dutch province of Drenthe, known for its scenic landscapes and prehistoric burial mounds.
- 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: Blankenfelde Triple: [Pankow, contains, Blankenfelde]
Generated description
Blankenfelde is a locality within the Berlin borough of Pankow, known for its residential character and proximity to green spaces.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Blankenfelde Target entity description: Blankenfelde is a locality within the Berlin borough of Pankow, known for its residential character and proximity to green spaces.
-
A.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
B.
Boven Pekela
Boven Pekela is a village in the municipality of Pekela in the province of Groningen in the northeastern Netherlands.
-
C.
Brackenheim
Brackenheim is a small town in the German state of Baden-Württemberg, best known as the birthplace of Theodor Heuss, the first President of the Federal Republic of Germany.
-
D.
Falkeplatz
Falkeplatz is a location in Chemnitz, Germany, known for hosting cultural institutions such as the Museum Gunzenhauser.
-
E.
Havelterberg
Havelterberg is a modest hill and natural area in the Dutch province of Drenthe, known for its scenic landscapes and prehistoric burial mounds.
- 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_69aed96ce578819084ab16e3439976c9 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeebb397ac81908f74a42a0eeb8682 |
completed | March 9, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5040d478081909a903bbf02f0d0ec |
completed | March 14, 2026, 6:45 a.m. |
| NEDg | Description generation | batch_69b50492239c8190a6c62504e2a6d130 |
completed | March 14, 2026, 6:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b50863e4a08190bd54274b2212abfc |
completed | March 14, 2026, 7:04 a.m. |
Created at: March 9, 2026, 3:18 p.m.