Triple
T7481570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koszalin |
E176769
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Sønderborg |
E521588
|
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: Sønderborg | Statement: [Koszalin, twinTown, Sønderborg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sønderborg Context triple: [Koszalin, twinTown, Sønderborg]
-
A.
Sønderborg
chosen
Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
-
B.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
C.
Skanderborg
Skanderborg is a Danish town in Jutland known for its lakeside setting and annual music festival, Smukfest.
-
D.
Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
-
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 (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_69c69f236ce08190a04d7679f03b29b2 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f534aa388190b3bb3e16be3a54c8 |
completed | March 27, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c845fd62dc8190a64f5464a204c0d2 |
completed | March 28, 2026, 9:19 p.m. |
Created at: March 27, 2026, 3:42 p.m.