Triple
T17686903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gribskov Line |
E440915
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Gilleleje |
—
|
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: Gilleleje | Statement: [Gribskov Line, connects, Gilleleje]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gilleleje Context triple: [Gribskov Line, connects, Gilleleje]
-
A.
Gilleleje
chosen
Gilleleje is a coastal town and fishing port in northern Zealand, Denmark, known for its harbor, beaches, and maritime heritage.
-
B.
Grenaa
Grenaa is a coastal town in eastern Jutland, Denmark, known for its ferry connections to the island of Anholt and its role as a regional commercial and educational center.
-
C.
Farsø
Farsø is a small Danish town in North Jutland, best known as the birthplace of Nobel Prize–winning author Johannes V. Jensen.
-
D.
Lemvig
Lemvig is a small coastal town in western Denmark known for its harbor, hilly landscape, and location along the Limfjord.
-
E.
Faaborg
Faaborg is a historic coastal town on the island of Funen in southern Denmark, known for its well-preserved old town, harbor, and cultural attractions.
- 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.