Triple
T7542153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jever |
E178304
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Wilhelmshaven |
E41561
|
NE FINISHED |
How this triple was built (2 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: Wilhelmshaven | Statement: [Jever, locatedNear, Wilhelmshaven]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilhelmshaven Context triple: [Jever, locatedNear, Wilhelmshaven]
-
A.
Wilhelmshaven
chosen
Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
-
B.
Cuxhaven
Cuxhaven is a German port city on the North Sea coast that historically served as an important naval and maritime hub.
-
C.
Dover
Dover is one of the oldest permanent European settlements in what is now the U.S. state of New Hampshire, historically significant as an early colonial town and port.
-
D.
Dover
Dover is a small town in eastern Dutchess County, New York, known for its rural character and location near the Connecticut border.
-
E.
Dover
Dover is a coastal town in southeast England best known for its white chalk cliffs and its strategic port facing the narrowest part of the English Channel.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c69f2be3888190a6667a27f8f195e9 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8762b048190a0b262f9cb3fe1b0 |
completed | March 27, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84f1d1e148190be015c62ae1ea1e8 |
completed | March 28, 2026, 9:58 p.m. |
Created at: March 27, 2026, 3:48 p.m.