Triple
T2515879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Island of Usedom |
E55410
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Karlshagen
Karlshagen is a seaside resort village on the Baltic coast of northeastern Germany, located on the island of Usedom and known for its sandy beaches and tourism.
|
E291724
|
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: Karlshagen | Statement: [Island of Usedom, hasPart, Karlshagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karlshagen Context triple: [Island of Usedom, hasPart, Karlshagen]
-
A.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
B.
Stolberg
Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
-
C.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
D.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
E.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
- 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: Karlshagen Triple: [Island of Usedom, hasPart, Karlshagen]
Generated description
Karlshagen is a seaside resort village on the Baltic coast of northeastern Germany, located on the island of Usedom and known for its sandy beaches and tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karlshagen Target entity description: Karlshagen is a seaside resort village on the Baltic coast of northeastern Germany, located on the island of Usedom and known for its sandy beaches and tourism.
-
A.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
B.
Stolberg
Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
-
C.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
D.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
E.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd20db7e0819096d901eb20ae65e5 |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb66a2c588190874782f4565c0b60 |
completed | March 10, 2026, 6:12 a.m. |
| NEDg | Description generation | batch_69afb7166d788190ac219fe3c3e164fe |
completed | March 10, 2026, 6:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb7aa131c81908cdfbda9575312f3 |
completed | March 10, 2026, 6:18 a.m. |
Created at: March 6, 2026, 9:46 p.m.