Triple
T3041397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oberharz am Brocken |
E83137
|
entity |
| Predicate | formedByMergerOf |
P77
|
FINISHED |
| Object |
Rübeland
Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
|
E328446
|
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: Rübeland | Statement: [Oberharz am Brocken, formedByMergerOf, Rübeland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rübeland Context triple: [Oberharz am Brocken, formedByMergerOf, Rübeland]
-
A.
Kellerwald
Kellerwald is a low mountain forest region in central Germany known for its ancient beech woodlands and protected national park status.
-
B.
Flachsland
Flachsland is a German-language surname associated with individuals such as Maria Karoline Flachsland.
-
C.
Solling
Solling is a forested low mountain range in Lower Saxony, Germany, known for its extensive woodlands and role as a major part of the Weser Uplands.
-
D.
Wiehe
Wiehe is a small town in the German state of Thuringia, historically notable as the birthplace of the influential 19th-century historian Leopold von Ranke.
-
E.
Hesse
Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
- 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: Rübeland Triple: [Oberharz am Brocken, formedByMergerOf, Rübeland]
Generated description
Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rübeland Target entity description: Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
-
A.
Kellerwald
Kellerwald is a low mountain forest region in central Germany known for its ancient beech woodlands and protected national park status.
-
B.
Flachsland
Flachsland is a German-language surname associated with individuals such as Maria Karoline Flachsland.
-
C.
Solling
Solling is a forested low mountain range in Lower Saxony, Germany, known for its extensive woodlands and role as a major part of the Weser Uplands.
-
D.
Wiehe
Wiehe is a small town in the German state of Thuringia, historically notable as the birthplace of the influential 19th-century historian Leopold von Ranke.
-
E.
Hesse
Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b5b92088190971bed04e65c5917 |
completed | March 8, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f41e2dc8190bba90185edd287ac |
completed | March 12, 2026, 12:56 a.m. |
| NEDg | Description generation | batch_69b20ffa40888190a9a1b048cc1d7454 |
completed | March 12, 2026, 12:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b21053de288190a73f09b64041af9e |
completed | March 12, 2026, 1:01 a.m. |
Created at: March 8, 2026, 3:01 p.m.