Triple
T8270599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karol Lipiński |
E193417
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object |
Ustronie, Poland
Ustronie is a locality in Poland known historically as the place where the renowned Polish violinist and composer Karol Lipiński died.
|
E726341
|
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: Ustronie, Poland | Statement: [Karol Lipiński, placeOfDeath, Ustronie, Poland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ustronie, Poland Context triple: [Karol Lipiński, placeOfDeath, Ustronie, Poland]
-
A.
Lutynia, Poland
Lutynia, Poland is a village in southwestern Poland best known as the site of the historic 1757 Battle of Leuthen during the Seven Years' War.
-
B.
Żarnowiec, Poland
Żarnowiec, Poland is a small village in northern Poland known for its historic monastery and scenic rural surroundings.
-
C.
Kozienice, Poland
Kozienice is a historic town in east-central Poland known for its location along the Vistula River and proximity to the Kozienice Landscape Park.
-
D.
Popowo, Poland
Popowo, Poland is a small Polish village best known as the birthplace of former president and Solidarity leader Lech Wałęsa.
-
E.
Tychy, Poland
Tychy, Poland is an industrial city in the Silesian region known for its major automotive manufacturing plants and brewing industry.
- 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: Ustronie, Poland Triple: [Karol Lipiński, placeOfDeath, Ustronie, Poland]
Generated description
Ustronie is a locality in Poland known historically as the place where the renowned Polish violinist and composer Karol Lipiński died.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ustronie, Poland Target entity description: Ustronie is a locality in Poland known historically as the place where the renowned Polish violinist and composer Karol Lipiński died.
-
A.
Lutynia, Poland
Lutynia, Poland is a village in southwestern Poland best known as the site of the historic 1757 Battle of Leuthen during the Seven Years' War.
-
B.
Żarnowiec, Poland
Żarnowiec, Poland is a small village in northern Poland known for its historic monastery and scenic rural surroundings.
-
C.
Kozienice, Poland
Kozienice is a historic town in east-central Poland known for its location along the Vistula River and proximity to the Kozienice Landscape Park.
-
D.
Popowo, Poland
Popowo, Poland is a small Polish village best known as the birthplace of former president and Solidarity leader Lech Wałęsa.
-
E.
Tychy, Poland
Tychy, Poland is an industrial city in the Silesian region known for its major automotive manufacturing plants and brewing industry.
- 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_69ca82e14ae481908ffdb822cd2192bc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb795243fc8190a66afef7476e1147 |
completed | March 31, 2026, 7:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd9510a74c8190a8f8c9c7e430b5ae |
completed | April 1, 2026, 9:58 p.m. |
| NEDg | Description generation | batch_69cdab59ac188190ac017651b5a9a04a |
completed | April 1, 2026, 11:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cdb2ae376c8190b3918ba6b269dba9 |
completed | April 2, 2026, 12:05 a.m. |
Created at: March 30, 2026, 5:50 p.m.