Triple
T5132576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruhr area |
E115735
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Gladbeck
Gladbeck is a town in western Germany’s North Rhine-Westphalia, historically shaped by coal mining and now part of the broader Ruhr industrial region.
|
E495844
|
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: Gladbeck | Statement: [Ruhr area, containsCity, Gladbeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gladbeck Context triple: [Ruhr area, containsCity, Gladbeck]
-
A.
Bergkamen
Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
-
B.
Hörden
Hörden is a district of the town of Gaggenau in the Rastatt district of Baden-Württemberg, Germany, known for its location in the northern Black Forest region.
-
C.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
D.
Euskirchen
Euskirchen is a town in the German state of North Rhine-Westphalia, known as a regional center near Bonn and the Eifel region.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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: Gladbeck Triple: [Ruhr area, containsCity, Gladbeck]
Generated description
Gladbeck is a town in western Germany’s North Rhine-Westphalia, historically shaped by coal mining and now part of the broader Ruhr industrial region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gladbeck Target entity description: Gladbeck is a town in western Germany’s North Rhine-Westphalia, historically shaped by coal mining and now part of the broader Ruhr industrial region.
-
A.
Bergkamen
Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
-
B.
Hörden
Hörden is a district of the town of Gaggenau in the Rastatt district of Baden-Württemberg, Germany, known for its location in the northern Black Forest region.
-
C.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
D.
Euskirchen
Euskirchen is a town in the German state of North Rhine-Westphalia, known as a regional center near Bonn and the Eifel region.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd784b477c8190926daddb28a255af |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4c9a14881908a8bf2f73ebf56f7 |
completed | March 21, 2026, 4:18 p.m. |
| NEDg | Description generation | batch_69bec562d0508190851b5a3307e9405b |
completed | March 21, 2026, 4:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bec6478b848190bc09d7f6485681b4 |
completed | March 21, 2026, 4:24 p.m. |
Created at: March 20, 2026, 1:42 p.m.