Triple
T12566892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Westphalia |
E295497
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Herdecke
Herdecke is a small town in western Germany known for its scenic location on the Ruhr River and its historic half-timbered architecture.
|
E1070025
|
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: Herdecke | Statement: [Province of Westphalia, containsSettlement, Herdecke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herdecke Context triple: [Province of Westphalia, containsSettlement, Herdecke]
-
A.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
B.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
C.
Dorsten
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
-
D.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
E.
Diepholz
Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
- 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: Herdecke Triple: [Province of Westphalia, containsSettlement, Herdecke]
Generated description
Herdecke is a small town in western Germany known for its scenic location on the Ruhr River and its historic half-timbered architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Herdecke Target entity description: Herdecke is a small town in western Germany known for its scenic location on the Ruhr River and its historic half-timbered architecture.
-
A.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
B.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
C.
Dorsten
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
-
D.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
E.
Diepholz
Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d954a325948190994bcfc9d571a3a8 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce5b2b988190892e14620fb87366 |
completed | May 3, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69f9fd56da288190b2bd33bc496c3fb9 |
completed | May 5, 2026, 2:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fb039fdb1c8190ad5286d1cfe80a29 |
completed | May 6, 2026, 9:02 a.m. |
Created at: April 8, 2026, 11:49 p.m.