Triple
T12600286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergisches Land |
E300838
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Marienheide
Marienheide is a municipality in North Rhine-Westphalia, Germany, situated in the hilly, forested region of the Bergisches Land.
|
E991904
|
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: Marienheide | Statement: [Bergisches Land, contains, Marienheide]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marienheide Context triple: [Bergisches Land, contains, Marienheide]
-
A.
Halensee
Halensee is a railway station in Berlin that serves the city's circular Ringbahn line, connecting the Halensee district to the wider urban rail network.
-
B.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
Wuhlheide
Wuhlheide is a large forested park and recreational area in Berlin known for its green spaces, outdoor activities, and cultural venues.
-
E.
Birkenwerder
Birkenwerder is a small municipality in the German state of Brandenburg, located just north of Berlin and known for its residential character and surrounding forests.
- 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: Marienheide Triple: [Bergisches Land, contains, Marienheide]
Generated description
Marienheide is a municipality in North Rhine-Westphalia, Germany, situated in the hilly, forested region of the Bergisches Land.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marienheide Target entity description: Marienheide is a municipality in North Rhine-Westphalia, Germany, situated in the hilly, forested region of the Bergisches Land.
-
A.
Halensee
Halensee is a railway station in Berlin that serves the city's circular Ringbahn line, connecting the Halensee district to the wider urban rail network.
-
B.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
Wuhlheide
Wuhlheide is a large forested park and recreational area in Berlin known for its green spaces, outdoor activities, and cultural venues.
-
E.
Birkenwerder
Birkenwerder is a small municipality in the German state of Brandenburg, located just north of Berlin and known for its residential character and surrounding forests.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954d1f6ac8190ab21ca7bcbc80129 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ec92c6c8190bd2d193e70940407 |
completed | May 2, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f65faf33e0819092df07a5fa98cb73 |
completed | May 2, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f66036f520819098af75cd5578d573 |
completed | May 2, 2026, 8:36 p.m. |
Created at: April 9, 2026, 5:09 p.m.