Triple
T1456307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Silesia |
E31408
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kłodzko
Kłodzko is a historic town in southwestern Poland known for its well-preserved medieval architecture and prominent hilltop fortress.
|
E217920
|
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: Kłodzko | Statement: [Lower Silesia, contains, Kłodzko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kłodzko Context triple: [Lower Silesia, contains, Kłodzko]
-
A.
Krosno
Krosno is a historic town in southeastern Poland known for its glassmaking industry and well-preserved old town.
-
B.
Glogów
Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
-
C.
Włoszczowa
Włoszczowa is a town in south-central Poland known as the seat of Włoszczowa County and a local administrative and service center.
-
D.
Bielsko-Biała
Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
-
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. 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: Kłodzko Triple: [Lower Silesia, contains, Kłodzko]
Generated description
Kłodzko is a historic town in southwestern Poland known for its well-preserved medieval architecture and prominent hilltop fortress.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kłodzko Target entity description: Kłodzko is a historic town in southwestern Poland known for its well-preserved medieval architecture and prominent hilltop fortress.
-
A.
Krosno
Krosno is a historic town in southeastern Poland known for its glassmaking industry and well-preserved old town.
-
B.
Glogów
Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
-
C.
Włoszczowa
Włoszczowa is a town in south-central Poland known as the seat of Włoszczowa County and a local administrative and service center.
-
D.
Bielsko-Biała
Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
-
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. 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c581714881909bf4c2bad9645176 |
completed | March 1, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfb8d636c8190a8a9ca29d8a6fd82 |
completed | March 8, 2026, 10:43 p.m. |
| NEDg | Description generation | batch_69adfc9a4060819085d69a8642e49673 |
completed | March 8, 2026, 10:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adfd03414c8190b1fc2b5608563726 |
completed | March 8, 2026, 10:49 p.m. |
Created at: March 1, 2026, 8 p.m.