Triple
T9495213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wagria |
E228986
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kellenhusen
Kellenhusen is a seaside resort town on the Baltic Sea coast of northern Germany, known for its beaches and tourism.
|
E806279
|
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: Kellenhusen | Statement: [Wagria, contains, Kellenhusen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kellenhusen Context triple: [Wagria, contains, Kellenhusen]
-
A.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
B.
Todenfeld
Todenfeld is a village and district of the town of Rheinbach in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
-
C.
Langenhain
Langenhain is a district of the town Hofheim am Taunus in the German state of Hesse, known for its residential character and proximity to the Taunus hills.
-
D.
Widdersberg
Widdersberg is a small village that forms one of the local subdivisions of the municipality of Münsing in Bavaria, Germany.
-
E.
Mülbracht
Mülbracht is a historical locality in the Holy Roman Empire known primarily as the birthplace of the Dutch Golden Age engraver and painter Hendrick Goltzius.
- 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: Kellenhusen Triple: [Wagria, contains, Kellenhusen]
Generated description
Kellenhusen is a seaside resort town on the Baltic Sea coast of northern Germany, known for its beaches and tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kellenhusen Target entity description: Kellenhusen is a seaside resort town on the Baltic Sea coast of northern Germany, known for its beaches and tourism.
-
A.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
B.
Todenfeld
Todenfeld is a village and district of the town of Rheinbach in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
-
C.
Langenhain
Langenhain is a district of the town Hofheim am Taunus in the German state of Hesse, known for its residential character and proximity to the Taunus hills.
-
D.
Widdersberg
Widdersberg is a small village that forms one of the local subdivisions of the municipality of Münsing in Bavaria, Germany.
-
E.
Mülbracht
Mülbracht is a historical locality in the Holy Roman Empire known primarily as the birthplace of the Dutch Golden Age engraver and painter Hendrick Goltzius.
- 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_69d1526994dc81908fe637f806ebf390 |
completed | April 4, 2026, 6:03 p.m. |
| NEDg | Description generation | batch_69d1538ad2308190aaa529b402e09eca |
completed | April 4, 2026, 6:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d153e07ac48190bb39edc64e03b57d |
completed | April 4, 2026, 6:09 p.m. |
Created at: March 30, 2026, 7:56 p.m.