Triple
T546660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olkusz |
E12746
|
entity |
| Predicate | hasSportsClub |
P346
|
FINISHED |
| Object | KS Olkusz |
E12746
|
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: KS Olkusz | Statement: [Olkusz, hasSportsClub, KS Olkusz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KS Olkusz Context triple: [Olkusz, hasSportsClub, KS Olkusz]
-
A.
Skarżysko-Kamienna
Skarżysko-Kamienna is a town in south-central Poland known for its industrial heritage and location in the Świętokrzyskie Voivodeship.
-
B.
Glogów
Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
-
C.
Katowice
Katowice is a major industrial and cultural city in southern Poland, known as the capital of the Silesian region.
-
D.
Olkusz
chosen
Olkusz is a historic town in southern Poland known for its medieval silver and lead mining heritage and well-preserved Old Town.
-
E.
Chrzanów
Chrzanów is a town in southern Poland known for its historical architecture and role as a local industrial and administrative center.
- 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_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a498e150e88190b35b1bc7a376ca07 |
completed | March 1, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4d044b03c8190b7c87b0da05e4d6e |
completed | March 1, 2026, 11:48 p.m. |
Created at: March 1, 2026, 7:32 p.m.