Triple
T10095835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Møn |
E215862
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Bogø |
E823073
|
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: Bogø | Statement: [Møn, connectedTo, Bogø]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bogø Context triple: [Møn, connectedTo, Bogø]
-
A.
Bogesund
Bogesund is a locality in Sweden known for its surrounding archipelago landscape, forests, and recreational natural areas.
-
B.
Abildsø
Abildsø is a residential neighborhood in the borough of Østensjø in Oslo, Norway, known for its green areas and proximity to the lake Østensjøvannet.
-
C.
Birkelunden
Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
-
D.
Vækerø
Vækerø is a residential and commercial area in Oslo, Norway, located along the western waterfront and known for its mix of housing, offices, and green spaces.
-
E.
Nakskov
chosen
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
- 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_69d2b6b8d604819094db099981219e72 |
completed | April 5, 2026, 7:23 p.m. |
Created at: March 30, 2026, 9:02 p.m.