Triple
T9495217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wagria |
E228986
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Wangels
Wangels is a small municipality in the Wagria region of Schleswig-Holstein in northern Germany, known for its rural landscape and proximity to the Baltic Sea coast.
|
E802829
|
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: Wangels | Statement: [Wagria, contains, Wangels]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wangels Context triple: [Wagria, contains, Wangels]
-
A.
Wossek
Wossek is a small town in what is now the Czech Republic, historically part of the Austro-Hungarian Empire and known as the birthplace of Hermann Kafka, father of writer Franz Kafka.
-
B.
Wallot
Wallot is a German surname most notably associated with architect Paul Wallot, designer of the Reichstag building in Berlin.
-
C.
Würges
Würges is a district of the spa town Bad Camberg in the Limburg-Weilburg region of Hesse, Germany.
-
D.
Wilkasy
Wilkasy is a village and popular lakeside tourist resort in northeastern Poland’s Warmian-Masurian Voivodeship, known for its marinas and access to the Masurian Lake District.
-
E.
Wanze
Wanze is a municipality in eastern Belgium situated along the Meuse River in the Walloon Region.
- 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: Wangels Triple: [Wagria, contains, Wangels]
Generated description
Wangels is a small municipality in the Wagria region of Schleswig-Holstein in northern Germany, known for its rural landscape and proximity to the Baltic Sea coast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wangels Target entity description: Wangels is a small municipality in the Wagria region of Schleswig-Holstein in northern Germany, known for its rural landscape and proximity to the Baltic Sea coast.
-
A.
Wossek
Wossek is a small town in what is now the Czech Republic, historically part of the Austro-Hungarian Empire and known as the birthplace of Hermann Kafka, father of writer Franz Kafka.
-
B.
Wallot
Wallot is a German surname most notably associated with architect Paul Wallot, designer of the Reichstag building in Berlin.
-
C.
Würges
Würges is a district of the spa town Bad Camberg in the Limburg-Weilburg region of Hesse, Germany.
-
D.
Wilkasy
Wilkasy is a village and popular lakeside tourist resort in northeastern Poland’s Warmian-Masurian Voivodeship, known for its marinas and access to the Masurian Lake District.
-
E.
Wanze
Wanze is a municipality in eastern Belgium situated along the Meuse River in the Walloon Region.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95eb87b081908fc7255598cd9a24 |
completed | April 1, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12d34967881909980be6f1be80885 |
completed | April 4, 2026, 3:24 p.m. |
| NEDg | Description generation | batch_69d13113474881909201282ce1385073 |
completed | April 4, 2026, 3:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d131ade0588190bdf3cfdbbdd6df8e |
completed | April 4, 2026, 3:43 p.m. |
Created at: March 30, 2026, 7:56 p.m.