Triple
T9861717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zaleski |
E239729
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Załęski
Załęski is a Polish surname, a diacritic variant of Zaleski, borne by individuals and families of Polish origin.
|
E828774
|
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: Załęski | Statement: [Zaleski, hasVariant, Załęski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Załęski Context triple: [Zaleski, hasVariant, Załęski]
-
A.
Rakoń
Rakoń is a mountain peak in the Western Tatras on the Polish-Slovak border, popular with hikers for its scenic ridge views.
-
B.
Łęczna
Łęczna is a town in eastern Poland known for its location near the Lublin Coal Basin and as a local administrative and service center.
-
C.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
-
D.
Lechia
Lechia is a Polish professional football club based in Gdańsk, known for competing in the country’s top leagues and having a passionate local fanbase.
-
E.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
- 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: Załęski Triple: [Zaleski, hasVariant, Załęski]
Generated description
Załęski is a Polish surname, a diacritic variant of Zaleski, borne by individuals and families of Polish origin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Załęski Target entity description: Załęski is a Polish surname, a diacritic variant of Zaleski, borne by individuals and families of Polish origin.
-
A.
Rakoń
Rakoń is a mountain peak in the Western Tatras on the Polish-Slovak border, popular with hikers for its scenic ridge views.
-
B.
Łęczna
Łęczna is a town in eastern Poland known for its location near the Lublin Coal Basin and as a local administrative and service center.
-
C.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
-
D.
Lechia
Lechia is a Polish professional football club based in Gdańsk, known for competing in the country’s top leagues and having a passionate local fanbase.
-
E.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
- 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_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3b6aa108190978f1c0cdc0f45a0 |
completed | April 2, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d511e348190aab23a45048ea7b3 |
completed | April 5, 2026, 7:20 a.m. |
| NEDg | Description generation | batch_69d20e9f480c819086b0165aa77ddb06 |
completed | April 5, 2026, 7:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d20fa9cab88190bbddcf18b49f8172 |
completed | April 5, 2026, 7:30 a.m. |
Created at: March 30, 2026, 8:35 p.m.