Triple
T3361529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Poland |
E70731
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Rybnik
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.
|
E351435
|
NE FINISHED |
How this triple was built (4 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: [Southern Poland, hasMajorCity, Rybnik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rybnik Context triple: [Southern Poland, hasMajorCity, Rybnik]
-
A.
Dąbie
Dąbie is a small town in central Poland, located in the Łódź Voivodeship along the Ner River.
-
B.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
-
C.
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.
-
D.
Mława
Mława is a town in north-central Poland known for its historical significance, including a major World War II battle, and its regional cultural and economic role.
-
E.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rybnik Triple: [Southern Poland, hasMajorCity, Rybnik]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rybnik Target entity description: 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.
-
A.
Dąbie
Dąbie is a small town in central Poland, located in the Łódź Voivodeship along the Ner River.
-
B.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
-
C.
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.
-
D.
Mława
Mława is a town in north-central Poland known for its historical significance, including a major World War II battle, and its regional cultural and economic role.
-
E.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
- F. None of above. chosen
Provenance (5 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_69ad85a660c48190998489309a3b4869 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb26906948190851a7b7d543a4d64 |
completed | March 8, 2026, 5:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b32546651481908dc6ab15b3344788 |
completed | March 12, 2026, 8:42 p.m. |
| NEDg | Description generation | batch_69b3280b712c8190875d5d35a1795bcd |
completed | March 12, 2026, 8:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b328ad98cc819092f5b92eb4abd1b7 |
completed | March 12, 2026, 8:57 p.m. |
Created at: March 8, 2026, 3:13 p.m.