Triple
T22331969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Öömrang |
E552044
|
entity |
| Predicate | exonym |
P4705
|
FINISHED |
| Object | Amrum Frisian |
—
|
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: Amrum Frisian | Statement: [Öömrang, exonym, Amrum Frisian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amrum Frisian Context triple: [Öömrang, exonym, Amrum Frisian]
-
A.
Halligen Frisian
Halligen Frisian is a distinctive variety of the North Frisian language spoken on the small Halligen islands off the coast of Germany.
-
B.
Amrum
chosen
Amrum is a North Frisian island in the North Sea known for its wide sandy beaches, dunes, and traditional thatched-roof villages.
-
C.
Föhr
Föhr is a North Frisian island off the coast of Schleswig-Holstein in northern Germany, known for its mild climate, sandy beaches, and traditional Frisian culture.
-
D.
Pellworm
Pellworm is a low-lying, dike-protected island in the North Sea off the coast of northern Germany, known for its tranquil rural landscape, birdlife, and reliance on renewable energy.
-
E.
Sylt
Sylt is a popular German North Sea island known for its long sandy beaches, distinctive dune landscapes, and status as an upscale holiday destination.
- 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577b555c8190ac61c026ee7dfb2b |
completed | April 29, 2026, 12:57 a.m. |
Created at: April 16, 2026, 8:43 p.m.