Triple
T2144661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imperial German Navy |
E47035
|
entity |
| Predicate | secondaryBase |
P8522
|
FINISHED |
| Object |
Cuxhaven
Cuxhaven is a German port city on the North Sea coast that historically served as an important naval and maritime hub.
|
E238867
|
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: Cuxhaven | Statement: [Imperial German Navy, secondaryBase, Cuxhaven]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuxhaven Context triple: [Imperial German Navy, secondaryBase, Cuxhaven]
-
A.
Wilhelmshaven
Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
-
B.
Aurich
Aurich is a historic town in northwestern Germany that serves as one of the principal urban centers of the East Frisia region in Lower Saxony.
-
C.
Bremerhaven
Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
-
D.
Port of Flensburg
The Port of Flensburg is a small commercial and ferry harbor on the Flensburg Fjord near the German-Danish border, serving regional maritime trade and tourism.
-
E.
Wolfenbüttel
Wolfenbüttel is a historic town in Lower Saxony, Germany, known for its Renaissance castle and rich cultural heritage.
- 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: Cuxhaven Triple: [Imperial German Navy, secondaryBase, Cuxhaven]
Generated description
Cuxhaven is a German port city on the North Sea coast that historically served as an important naval and maritime hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cuxhaven Target entity description: Cuxhaven is a German port city on the North Sea coast that historically served as an important naval and maritime hub.
-
A.
Wilhelmshaven
Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
-
B.
Aurich
Aurich is a historic town in northwestern Germany that serves as one of the principal urban centers of the East Frisia region in Lower Saxony.
-
C.
Bremerhaven
Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
-
D.
Port of Flensburg
The Port of Flensburg is a small commercial and ferry harbor on the Flensburg Fjord near the German-Danish border, serving regional maritime trade and tourism.
-
E.
Wolfenbüttel
Wolfenbüttel is a historic town in Lower Saxony, Germany, known for its Renaissance castle and rich cultural heritage.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe223f848190a60bd0f15aed1021 |
completed | March 7, 2026, 5:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae58d5535c8190b59293afe3a10834 |
completed | March 9, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_69ae597198b88190b0253aa121ed35e1 |
completed | March 9, 2026, 5:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5a02404c819088acf7c592cb2cae |
completed | March 9, 2026, 5:26 a.m. |
Created at: March 4, 2026, 7:44 p.m.