Triple
T1037346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kemi |
E22392
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Szczecin |
E27720
|
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: Szczecin | Statement: [Kemi, twinTown, Szczecin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szczecin Context triple: [Kemi, twinTown, Szczecin]
-
A.
Szczecin
chosen
Szczecin is a large Polish city and important maritime and industrial center in northwestern Poland, situated near the Baltic Sea and the German border.
-
B.
Gdańsk
Gdańsk is a major Polish port city on the Baltic Sea, known for its rich Hanseatic history, shipyards, and role in the origins of the Solidarity movement.
-
C.
Koszalin
Koszalin is a city in northwestern Poland near the Baltic Sea, known as a regional cultural and economic center.
-
D.
Gdynia
Gdynia is a major seaport city on Poland’s Baltic coast, developed rapidly in the 20th century into one of the country’s key maritime and economic centers.
-
E.
Kołobrzeg
Kołobrzeg is a historic Polish port and spa city on the Baltic Sea, known for its beaches, seaside resorts, and role as a popular tourist destination.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b82a1014819085bfc077e24c9742 |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeab2f888819093d2b3e49b105802 |
completed | March 8, 2026, 9:31 p.m. |
Created at: March 1, 2026, 7:41 p.m.