Triple
T6606370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Silesia |
E149128
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Rybnik |
E351435
|
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: Rybnik | Statement: [Upper Silesia, hasMajorCity, Rybnik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rybnik Context triple: [Upper Silesia, hasMajorCity, Rybnik]
-
A.
Rybnik
chosen
Rybnik is a significant industrial and cultural city in the Silesian region of southern Poland, known for its coal mining heritage and regional economic importance.
-
B.
Dąbie
Dąbie is a small town in central Poland, located in the Łódź Voivodeship along the Ner River.
-
C.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
-
D.
Bóbrka
Bóbrka is a village in southeastern Poland historically significant as one of the birthplaces of the modern oil industry.
-
E.
Rybi Potok
Rybi Potok is a mountain stream in the Tatra Mountains of southern Poland that drains the waters of the popular alpine lake Morskie Oko.
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af143d5c8190b62602602510b1cb |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbce25b481908d600d38d3c5b871 |
completed | March 27, 2026, 6:26 p.m. |
Created at: March 27, 2026, 1:57 p.m.