Triple
T10095909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region Zealand |
E215864
|
entity |
| Predicate | includesPartOf |
P35
|
FINISHED |
| Object | Møn |
E215862
|
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: Møn | Statement: [Region Zealand, includesPartOf, Møn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Møn Context triple: [Region Zealand, includesPartOf, Møn]
-
A.
Møn
chosen
Møn is a Danish island in the Baltic Sea known for its dramatic white chalk cliffs, scenic landscapes, and rich prehistoric and cultural heritage.
-
B.
Rømø
Rømø is a Danish island in the Wadden Sea known for its expansive sandy beaches, coastal dunes, and popular holiday resorts.
-
C.
Mandø
Mandø is a small Danish island in the Wadden Sea, known for its tidal causeway access, rich birdlife, and traditional marshland landscapes.
-
D.
Ærø
Ærø is a small Danish island in the Baltic Sea known for its picturesque coastal towns, historic architecture, and popular cycling and sailing routes.
-
E.
Langeland
Langeland is a Danish island in the South Funen Archipelago, known for its rural landscapes, coastal scenery, and historical villages.
- 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_69ca83a4947c8190823a7495dc5d96ed |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd0798c248190af675e30e280daa8 |
completed | April 2, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6528677f88190b259d5a25ddc290b |
completed | April 8, 2026, 1:05 p.m. |
Created at: March 30, 2026, 9:02 p.m.