Triple
T12566860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Westphalia |
E295497
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Harsewinkel
Harsewinkel is a town in the German state of North Rhine-Westphalia, known for its agricultural machinery industry and rural character.
|
E990669
|
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: Harsewinkel | Statement: [Province of Westphalia, containsSettlement, Harsewinkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harsewinkel Context triple: [Province of Westphalia, containsSettlement, Harsewinkel]
-
A.
Gailingen
Gailingen is a village in the German municipality of Gailingen am Hochrhein in the state of Baden-Württemberg, near the Swiss border along the High Rhine.
-
B.
Rheinhausen
Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
-
C.
Wesseling
Wesseling is a German industrial town on the Rhine River in North Rhine-Westphalia, situated between Cologne and Bonn.
-
D.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
E.
Barßel
Barßel is a small municipality in Lower Saxony, Germany, known for its location in the moor and wetlands region of the Cloppenburg district.
- 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: Harsewinkel Triple: [Province of Westphalia, containsSettlement, Harsewinkel]
Generated description
Harsewinkel is a town in the German state of North Rhine-Westphalia, known for its agricultural machinery industry and rural character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Harsewinkel Target entity description: Harsewinkel is a town in the German state of North Rhine-Westphalia, known for its agricultural machinery industry and rural character.
-
A.
Gailingen
Gailingen is a village in the German municipality of Gailingen am Hochrhein in the state of Baden-Württemberg, near the Swiss border along the High Rhine.
-
B.
Rheinhausen
Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
-
C.
Wesseling
Wesseling is a German industrial town on the Rhine River in North Rhine-Westphalia, situated between Cologne and Bonn.
-
D.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
E.
Barßel
Barßel is a small municipality in Lower Saxony, Germany, known for its location in the moor and wetlands region of the Cloppenburg 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_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_69f655914f908190afbebbec3cb57e73 |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f657e504c881909b960acc7758b39d |
completed | May 2, 2026, 8 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f658a80fd08190b1b8c161ca6e56ec |
completed | May 2, 2026, 8:03 p.m. |
Created at: April 8, 2026, 11:49 p.m.