Triple
T10012495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baranya County |
E199407
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Szentlőrinc
Szentlőrinc is a small town in southern Hungary known for its agricultural surroundings and role as a local service and transport hub in Baranya County.
|
E949492
|
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: Szentlőrinc | Statement: [Baranya County, containsTown, Szentlőrinc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szentlőrinc Context triple: [Baranya County, containsTown, Szentlőrinc]
-
A.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
B.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
-
C.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
D.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
-
E.
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.
- 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: Szentlőrinc Triple: [Baranya County, containsTown, Szentlőrinc]
Generated description
Szentlőrinc is a small town in southern Hungary known for its agricultural surroundings and role as a local service and transport hub in Baranya County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Szentlőrinc Target entity description: Szentlőrinc is a small town in southern Hungary known for its agricultural surroundings and role as a local service and transport hub in Baranya County.
-
A.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
B.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
-
C.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
D.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
-
E.
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.
- 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_69f1658e03a8819098ea2ac2f818a61a |
completed | April 29, 2026, 1:57 a.m. |
| NEDg | Description generation | batch_69f16e31ebfc81908255e24b96bf9a99 |
completed | April 29, 2026, 2:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f1a09eae7481908200709ae9721d53 |
completed | April 29, 2026, 6:09 a.m. |
Created at: March 30, 2026, 8:52 p.m.