Triple
T6487785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beuel |
E146557
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Beuel-Mitte
Beuel-Mitte is the central district of the Beuel borough in Bonn, Germany, serving as its main urban and commercial area.
|
E595681
|
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: Beuel-Mitte | Statement: [Beuel, hasPart, Beuel-Mitte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beuel-Mitte Context triple: [Beuel, hasPart, Beuel-Mitte]
-
A.
Oststadt
Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
-
B.
Bahnhofsviertel
Bahnhofsviertel is a central Frankfurt district known for its mix of historic Wilhelminian architecture, nightlife, red-light area, and growing creative and gastronomic scene.
-
C.
Maxvorstadt
Maxvorstadt is a central Munich district known for its concentration of major art museums, universities, and cultural institutions.
-
D.
Stadtmitte
Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
-
E.
Stadtmitte
Stadtmitte is the central urban district of the town of Bad Honnef in North Rhine-Westphalia, Germany.
- 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: Beuel-Mitte Triple: [Beuel, hasPart, Beuel-Mitte]
Generated description
Beuel-Mitte is the central district of the Beuel borough in Bonn, Germany, serving as its main urban and commercial area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beuel-Mitte Target entity description: Beuel-Mitte is the central district of the Beuel borough in Bonn, Germany, serving as its main urban and commercial area.
-
A.
Oststadt
Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
-
B.
Bahnhofsviertel
Bahnhofsviertel is a central Frankfurt district known for its mix of historic Wilhelminian architecture, nightlife, red-light area, and growing creative and gastronomic scene.
-
C.
Maxvorstadt
Maxvorstadt is a central Munich district known for its concentration of major art museums, universities, and cultural institutions.
-
D.
Stadtmitte
Stadtmitte is the central urban district of the town of Bad Honnef in North Rhine-Westphalia, Germany.
-
E.
Stadtmitte
Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a96a4048190a28dee5fd9258486 |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c653b792f48190b301cdc643db8ddf |
completed | March 27, 2026, 9:53 a.m. |
| NEDg | Description generation | batch_69c6545575788190acb374fcdb7f5edf |
completed | March 27, 2026, 9:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c654c0cdf88190994217223fb2f77a |
completed | March 27, 2026, 9:58 a.m. |
Created at: March 22, 2026, 4:52 p.m.