Triple
T12813989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fehmarn Belt |
E306342
|
entity |
| Predicate | nearbyIsland |
P2064
|
FINISHED |
| Object | Lolland |
E220370
|
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: Lolland | Statement: [Fehmarn Belt, nearbyIsland, Lolland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lolland Context triple: [Fehmarn Belt, nearbyIsland, Lolland]
-
A.
Lolland
chosen
Lolland is a large, predominantly agricultural island in southeastern Denmark known for its flat landscape and sugar beet production.
-
B.
Bornholm
Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
-
C.
Djursland
Djursland is a rural peninsula in eastern Jutland, Denmark, known for its varied coastline, beaches, and popular holiday and nature tourism.
-
D.
Langeland
Langeland is a Danish island in the South Funen Archipelago, known for its rural landscapes, coastal scenery, and historical villages.
-
E.
Rømø
Rømø is a Danish island in the Wadden Sea known for its expansive sandy beaches, coastal dunes, and popular holiday resorts.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9adcf08190a12801adcc613477 |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a544badc8190877c39728e57af6f |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:31 p.m.