Triple
T7481571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koszalin |
E176769
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Słupsk |
E178809
|
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: Słupsk | Statement: [Koszalin, twinTown, Słupsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Słupsk Context triple: [Koszalin, twinTown, Słupsk]
-
A.
Słupsk
chosen
Słupsk is a historic city in northern Poland known for its medieval architecture and location near the Baltic Sea.
-
B.
Koszalin
Koszalin is a city in northwestern Poland near the Baltic Sea, known as a regional cultural and economic center.
-
C.
Świdwin
Świdwin is a historic town in northwestern Poland, known for its medieval castle and location in the West Pomeranian Voivodeship.
-
D.
Świnoujście
Świnoujście is a Polish port city and seaside resort on the Baltic Sea, known for its wide beaches, spa facilities, and strategic location at the mouth of the Świna River.
-
E.
Giżycko
Giżycko is a popular lakeside town in northeastern Poland, known as a major sailing and tourism center in the Masurian Lake District.
- 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_69ce6c7d795081909fc85cc99e11c971 |
completed | April 2, 2026, 1:17 p.m. |
Created at: March 27, 2026, 3:42 p.m.