Triple
T17686950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederiksværk Line |
E440916
|
entity |
| Predicate | connectsTown |
P845
|
FINISHED |
| Object | Hundested |
—
|
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: Hundested | Statement: [Frederiksværk Line, connectsTown, Hundested]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hundested Context triple: [Frederiksværk Line, connectsTown, Hundested]
-
A.
Hundested
chosen
Hundested is a small coastal town in North Zealand, Denmark, known for its harbor, beaches, and maritime activities.
-
B.
Næstved
Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
-
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.
Hornbæk
Hornbæk is a coastal town in northern Zealand, Denmark, known for its sandy beaches, holiday villas, and role as a popular seaside resort.
-
E.
Vollebæk
Vollebæk is a Norwegian surname most notably associated with diplomat and former foreign minister Knut Vollebæk.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e470488c4081909b747313ef97b69c |
completed | April 19, 2026, 6:03 a.m. |
Created at: April 10, 2026, 10:03 a.m.