Triple
T10852230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herning Station |
E256173
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Skjern |
E711973
|
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: Skjern | Statement: [Herning Station, connectsTo, Skjern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skjern Context triple: [Herning Station, connectsTo, Skjern]
-
A.
Skjern
chosen
Skjern is a town in western Jutland, Denmark, known for its location near the Skjern River and its surrounding agricultural landscape.
-
B.
Tønder
Tønder is a historic market town in southern Denmark near the German border, known for its well-preserved old town and cultural heritage.
-
C.
Næstved
Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
-
D.
Hellebæk
Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
-
E.
Slagelse
Slagelse is a town on the island of Zealand in Denmark known for its military presence, historical significance, and role as a regional commercial center.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75117b76c8190b0fb216b1428c3c7 |
completed | April 9, 2026, 7:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d4ab9bc81908a2522d1334390fc |
completed | April 19, 2026, 1:18 a.m. |
Created at: April 8, 2026, 9:20 p.m.