Triple
T12566999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Westphalia |
E295497
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Wabern
Wabern is a settlement located within the historical Province of Westphalia in Germany.
|
E1017293
|
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: Wabern | Statement: [Province of Westphalia, containsSettlement, Wabern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wabern Context triple: [Province of Westphalia, containsSettlement, Wabern]
-
A.
Meerbusch
Meerbusch is a town in the German state of North Rhine-Westphalia, situated on the west bank of the Rhine near Düsseldorf and known for its affluent residential areas and green surroundings.
-
B.
Scheyern
Scheyern is a Bavarian municipality best known as the site of Scheyern Abbey, a historic Benedictine monastery and ancestral seat of the Wittelsbach family.
-
C.
Riehe
Riehe is a small river in Lower Saxony, Germany, known as one of the tributaries feeding into the Innerste.
-
D.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
E.
Lahr
Lahr is a town in southwestern Germany’s Baden-Württemberg region, situated near the Rhine River opposite Strasbourg and known for its historic center and proximity to the Black Forest.
- 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: Wabern Triple: [Province of Westphalia, containsSettlement, Wabern]
Generated description
Wabern is a settlement located within the historical Province of Westphalia in Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wabern Target entity description: Wabern is a settlement located within the historical Province of Westphalia in Germany.
-
A.
Meerbusch
Meerbusch is a town in the German state of North Rhine-Westphalia, situated on the west bank of the Rhine near Düsseldorf and known for its affluent residential areas and green surroundings.
-
B.
Scheyern
Scheyern is a Bavarian municipality best known as the site of Scheyern Abbey, a historic Benedictine monastery and ancestral seat of the Wittelsbach family.
-
C.
Riehe
Riehe is a small river in Lower Saxony, Germany, known as one of the tributaries feeding into the Innerste.
-
D.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
E.
Lahr
Lahr is a town in southwestern Germany’s Baden-Württemberg region, situated near the Rhine River opposite Strasbourg and known for its historic center and proximity to the Black Forest.
- 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_69f6cbb096d881908dfd2a7126632d96 |
completed | May 3, 2026, 4:14 a.m. |
| NEDg | Description generation | batch_69f6cd3d5090819091b65f544ad139fd |
completed | May 3, 2026, 4:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6cdc8d52c819083717a455d589646 |
completed | May 3, 2026, 4:23 a.m. |
Created at: April 8, 2026, 11:49 p.m.