Triple
T2876271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiel Bay |
E56885
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Eckernförde |
E228983
|
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: Eckernförde | Statement: [Kiel Bay, hasNearbyCity, Eckernförde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eckernförde Context triple: [Kiel Bay, hasNearbyCity, Eckernförde]
-
A.
Eckernförde
chosen
Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
-
B.
Svinesund
Svinesund is a strait forming part of the border between Norway and Sweden, best known for its bridges and role as a major road crossing between the two countries.
-
C.
Itzehoe
Itzehoe is a historic town in northern Germany known for its medieval origins and role as a regional center in the state of Schleswig-Holstein.
-
D.
Rudkøbing
Rudkøbing is a small historic town on the Danish island of Langeland, known for its well-preserved old streets and as the birthplace of physicist Hans Christian Ørsted.
-
E.
Groß Borstel
Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe0061d048190bb1e5a01e7ceb0e2 |
completed | March 7, 2026, 8:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055edfd948190ad7433002efa3e53 |
completed | March 10, 2026, 5:33 p.m. |
Created at: March 6, 2026, 10:03 p.m.