Triple
T10012493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baranya County |
E199407
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Villány
Villány is a small town in southern Hungary renowned as one of the country’s premier wine regions, especially famous for its red wines.
|
E843689
|
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: Villány | Statement: [Baranya County, containsTown, Villány]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Villány Context triple: [Baranya County, containsTown, Villány]
-
A.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
-
B.
Devecser
Devecser is a small town in western Hungary known for its location in Veszprém County and for being affected by the 2010 Ajka alumina plant red sludge disaster.
-
C.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
D.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
-
E.
Gödöllő
Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
- 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: Villány Triple: [Baranya County, containsTown, Villány]
Generated description
Villány is a small town in southern Hungary renowned as one of the country’s premier wine regions, especially famous for its red wines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Villány Target entity description: Villány is a small town in southern Hungary renowned as one of the country’s premier wine regions, especially famous for its red wines.
-
A.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
-
B.
Devecser
Devecser is a small town in western Hungary known for its location in Veszprém County and for being affected by the 2010 Ajka alumina plant red sludge disaster.
-
C.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
D.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
-
E.
Gödöllő
Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
- 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd3cf5b881908f5318e55bdd22b6 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e5476f408190b8921cd4343c3af9 |
completed | April 5, 2026, 10:42 p.m. |
| NEDg | Description generation | batch_69d2e6f0aa988190aa9a866afcc2a1a2 |
completed | April 5, 2026, 10:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2e78384f48190abb7bdd7fcadcd9a |
completed | April 5, 2026, 10:51 p.m. |
Created at: March 30, 2026, 8:52 p.m.