Triple
T9749639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Homberg/Ruhrort/Baerl |
E236406
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Ruhrort
Ruhrort is a historic inland port district of Duisburg in western Germany, known for its major role in Rhine and Ruhr river shipping and industry.
|
E871100
|
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: Ruhrort | Statement: [Homberg/Ruhrort/Baerl, hasPart, Ruhrort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruhrort Context triple: [Homberg/Ruhrort/Baerl, hasPart, Ruhrort]
-
A.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
B.
Hennef
Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
-
C.
Gummersbach
Gummersbach is a town in North Rhine-Westphalia, Germany, known as a regional center in the Bergisches Land and a location for higher education and industry.
-
D.
Bergkamen
Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
-
E.
Bottrop
Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
- 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: Ruhrort Triple: [Homberg/Ruhrort/Baerl, hasPart, Ruhrort]
Generated description
Ruhrort is a historic inland port district of Duisburg in western Germany, known for its major role in Rhine and Ruhr river shipping and industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ruhrort Target entity description: Ruhrort is a historic inland port district of Duisburg in western Germany, known for its major role in Rhine and Ruhr river shipping and industry.
-
A.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
B.
Hennef
Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
-
C.
Gummersbach
Gummersbach is a town in North Rhine-Westphalia, Germany, known as a regional center in the Bergisches Land and a location for higher education and industry.
-
D.
Bergkamen
Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
-
E.
Bottrop
Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f6a2f8c8190a6f6af6587ee90b8 |
completed | April 1, 2026, 10:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9330fa33c8190b507ad18362a6c64 |
completed | April 10, 2026, 5:27 p.m. |
| NEDg | Description generation | batch_69d93802a4488190aa86ae209650d4e7 |
completed | April 10, 2026, 5:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d938fcc3c48190a4acaaf75c1aa304 |
completed | April 10, 2026, 5:53 p.m. |
Created at: March 30, 2026, 8:24 p.m.