Triple
T15128582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theodor Leschetizky |
E361356
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Łańcut
Łańcut is a historic town in southeastern Poland, renowned for its well-preserved castle and cultural heritage.
|
E1148558
|
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: Łańcut | Statement: [Theodor Leschetizky, placeOfBirth, Łańcut]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Łańcut Context triple: [Theodor Leschetizky, placeOfBirth, Łańcut]
-
A.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
-
B.
Oroszvár
Oroszvár is a historic locality in present-day western Slovakia (now part of Rusovce, a borough of Bratislava) known in part as a former residence of Princess Louise of Belgium.
-
C.
Kalocsa
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
D.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
E.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
- 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: Łańcut Triple: [Theodor Leschetizky, placeOfBirth, Łańcut]
Generated description
Łańcut is a historic town in southeastern Poland, renowned for its well-preserved castle and cultural heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Łańcut Target entity description: Łańcut is a historic town in southeastern Poland, renowned for its well-preserved castle and cultural heritage.
-
A.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
-
B.
Oroszvár
Oroszvár is a historic locality in present-day western Slovakia (now part of Rusovce, a borough of Bratislava) known in part as a former residence of Princess Louise of Belgium.
-
C.
Kalocsa
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
D.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
E.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005aff2648190bda885c09421758d |
completed | April 15, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef64dbfc819098dac50500673ed4 |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69fef1f731808190957aaa88a86b194c |
completed | May 9, 2026, 8:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef37d1b7081908c5b1be109f590d9 |
completed | May 9, 2026, 8:42 a.m. |
Created at: April 10, 2026, 3:06 a.m.