Triple
T19973966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederikshavn |
E493638
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Sæby |
—
|
NE NERFINISHED |
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: Sæby | Statement: [Frederikshavn, hasNearbySettlement, Sæby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sæby Context triple: [Frederikshavn, hasNearbySettlement, Sæby]
-
A.
Sæby
chosen
Sæby is a coastal town in northern Jutland, Denmark, known for its historic town center, marina, and sandy beaches along the Kattegat.
-
B.
Skjern
Skjern is a town in western Jutland, Denmark, known for its location near the Skjern River and its surrounding agricultural landscape.
-
C.
Hellebæk
Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
-
D.
Sakskøbing
Sakskøbing is a small town on the Danish island of Lolland, known for its historic church, harbor, and surrounding agricultural landscape.
-
E.
Rødby
Rødby is a small town on the Danish island of Lolland, known historically as a ferry port linking Denmark and Germany across the Baltic Sea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65bcb72048190aedb4f085ace0493 |
completed | April 20, 2026, 5 p.m. |
Created at: April 11, 2026, 3:23 p.m.