Triple
T12143147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ustka |
E289241
|
entity |
| Predicate | historicalName |
P65
|
FINISHED |
| Object |
Stolpmünde
Stolpmünde is the former German name for the Baltic Sea port town now known as Ustka in northern Poland.
|
E964649
|
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: Stolpmünde | Statement: [Ustka, historicalName, Stolpmünde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stolpmünde Context triple: [Ustka, historicalName, Stolpmünde]
-
A.
Ueckermünde
Ueckermünde is a small historic port town in northeastern Germany on the Szczecin Lagoon, known for its maritime heritage and access to the Baltic Sea.
-
B.
Sassnitz
Sassnitz is a port town on the Baltic Sea coast of Germany, located on the island of Rügen and known as a gateway to nearby national parks and chalk cliffs.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
E.
Malchow
Malchow is a small locality within the Berlin borough of Lichtenberg, known for its more rural character and green surroundings compared to the inner city.
- 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: Stolpmünde Triple: [Ustka, historicalName, Stolpmünde]
Generated description
Stolpmünde is the former German name for the Baltic Sea port town now known as Ustka in northern Poland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stolpmünde Target entity description: Stolpmünde is the former German name for the Baltic Sea port town now known as Ustka in northern Poland.
-
A.
Ueckermünde
Ueckermünde is a small historic port town in northeastern Germany on the Szczecin Lagoon, known for its maritime heritage and access to the Baltic Sea.
-
B.
Sassnitz
Sassnitz is a port town on the Baltic Sea coast of Germany, located on the island of Rügen and known as a gateway to nearby national parks and chalk cliffs.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
E.
Malchow
Malchow is a small locality within the Berlin borough of Lichtenberg, known for its more rural character and green surroundings compared to the inner city.
- 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_69d6ab4c6710819097a9d228382dde43 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915aacaa08190b31f54e230334406 |
completed | April 10, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f69501348190a9ac090c7db37c20 |
completed | May 2, 2026, 1:05 p.m. |
| NEDg | Description generation | batch_69f5ff815b748190932bfe163ea2847d |
completed | May 2, 2026, 1:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6018b4088819092b8b97089068fae |
completed | May 2, 2026, 1:52 p.m. |
Created at: April 8, 2026, 9:49 p.m.