Triple
T7506567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münsterland |
E177404
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Warendorf
Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
|
E756159
|
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: Warendorf | Statement: [Münsterland, hasCity, Warendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warendorf Context triple: [Münsterland, hasCity, Warendorf]
-
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.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
Recklinghausen
Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
-
D.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
E.
Wallenhorst
Wallenhorst is a municipality in Lower Saxony, Germany, located near the city of Osnabrück.
- 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: Warendorf Triple: [Münsterland, hasCity, Warendorf]
Generated description
Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Warendorf Target entity description: Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
-
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.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
Recklinghausen
Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
-
D.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
E.
Wallenhorst
Wallenhorst is a municipality in Lower Saxony, Germany, located near the city of Osnabrück.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b76a288190bb3608a5e3bfa212 |
completed | March 27, 2026, 9:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf51147c4c8190b3c893700f48fc54 |
completed | April 3, 2026, 5:33 a.m. |
| NEDg | Description generation | batch_69cf52f0886881909ceb9fbe54f84d11 |
completed | April 3, 2026, 5:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf53bc19fc81908f43c3fa29bae021 |
completed | April 3, 2026, 5:44 a.m. |
Created at: March 27, 2026, 3:45 p.m.