Triple
T13765637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duchy of Lauenburg |
E330732
|
entity |
| Predicate | significantTown |
P102273
|
FINISHED |
| Object |
Mölln
Mölln is a historic town in northern Germany, known for its medieval center and association with the folk figure Till Eulenspiegel.
|
E1058582
|
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: Mölln | Statement: [Duchy of Lauenburg, significantTown, Mölln]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mölln Context triple: [Duchy of Lauenburg, significantTown, Mölln]
-
A.
Güstrow
Güstrow is a historic town in northern Germany known for its Renaissance castle, brick Gothic cathedral, and association with sculptor Ernst Barlach.
-
B.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
C.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
D.
Eckernförde
Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
-
E.
Korsholm
Korsholm is a coastal municipality in western Finland, known for its largely Swedish-speaking population and proximity to the city of Vaasa in the Ostrobothnia 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: Mölln Triple: [Duchy of Lauenburg, significantTown, Mölln]
Generated description
Mölln is a historic town in northern Germany, known for its medieval center and association with the folk figure Till Eulenspiegel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mölln Target entity description: Mölln is a historic town in northern Germany, known for its medieval center and association with the folk figure Till Eulenspiegel.
-
A.
Güstrow
Güstrow is a historic town in northern Germany known for its Renaissance castle, brick Gothic cathedral, and association with sculptor Ernst Barlach.
-
B.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
C.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
D.
Eckernförde
Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
-
E.
Korsholm
Korsholm is a coastal municipality in western Finland, known for its largely Swedish-speaking population and proximity to the city of Vaasa in the Ostrobothnia 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0227f2c48190983ccc9395e4e7a2 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a864c0ac81909e5fcd134ea66414 |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a94390988190bcaedcda0b691fbb |
completed | May 3, 2026, 8 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a9fe250c8190b66062e7fcea8d86 |
completed | May 3, 2026, 8:03 p.m. |
Created at: April 9, 2026, 10:10 p.m.