Triple
T7766008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheinbach |
E176146
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Oberdrees
Oberdrees is a village-level district of the town of Rheinbach in the Rhein-Sieg-Kreis of North Rhine-Westphalia, Germany.
|
E687177
|
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: Oberdrees | Statement: [Rheinbach, hasSubdivision, Oberdrees]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oberdrees Context triple: [Rheinbach, hasSubdivision, Oberdrees]
-
A.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
B.
Hohegeiß
Hohegeiß is a mountain village and health resort in the Harz region of central Germany, known for its scenic landscapes and outdoor recreation.
-
C.
Kleeberg
Kleeberg is a Polish surname most notably associated with General Franciszek Kleeberg, a commander in the early stages of World War II.
-
D.
Luterbach
Luterbach is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Aare River.
-
E.
Unterweser
Unterweser is the name given to the lower tidal section of the Weser River in northwestern Germany, extending from near Bremen to its mouth at the North Sea.
- 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: Oberdrees Triple: [Rheinbach, hasSubdivision, Oberdrees]
Generated description
Oberdrees is a village-level district of the town of Rheinbach in the Rhein-Sieg-Kreis of North Rhine-Westphalia, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oberdrees Target entity description: Oberdrees is a village-level district of the town of Rheinbach in the Rhein-Sieg-Kreis of North Rhine-Westphalia, Germany.
-
A.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
B.
Hohegeiß
Hohegeiß is a mountain village and health resort in the Harz region of central Germany, known for its scenic landscapes and outdoor recreation.
-
C.
Kleeberg
Kleeberg is a Polish surname most notably associated with General Franciszek Kleeberg, a commander in the early stages of World War II.
-
D.
Luterbach
Luterbach is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Aare River.
-
E.
Unterweser
Unterweser is the name given to the lower tidal section of the Weser River in northwestern Germany, extending from near Bremen to its mouth at the North Sea.
- 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_69c69962923c8190ac74d28b4f9fe0a0 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7043279748190b30882e9cc6cca54 |
completed | March 27, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7e1a1408190a802d4f4afb8dd05 |
completed | March 29, 2026, 6:34 a.m. |
| NEDg | Description generation | batch_69c8c8b75b848190a67de2040d563f86 |
completed | March 29, 2026, 6:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c941814081909d299df5cd714c71 |
completed | March 29, 2026, 6:40 a.m. |
Created at: March 27, 2026, 4:09 p.m.