Triple
T9821880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bács-Kiskun County |
E238550
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Kiskunhalas
Kiskunhalas is a town in southern Hungary known for its traditional lace-making and agricultural economy.
|
E832536
|
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: Kiskunhalas | Statement: [Bács-Kiskun County, hasCity, Kiskunhalas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiskunhalas Context triple: [Bács-Kiskun County, hasCity, Kiskunhalas]
-
A.
Dunaharaszti
Dunaharaszti is a town in central Hungary that functions largely as a suburban residential and industrial area near Budapest.
-
B.
Bicske
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
-
C.
Bodrogköz
Bodrogköz is a low-lying, marshy region in northeastern Hungary known for its riverine landscapes, wetlands, and traditional rural settlements.
-
D.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
E.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
- 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: Kiskunhalas Triple: [Bács-Kiskun County, hasCity, Kiskunhalas]
Generated description
Kiskunhalas is a town in southern Hungary known for its traditional lace-making and agricultural economy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kiskunhalas Target entity description: Kiskunhalas is a town in southern Hungary known for its traditional lace-making and agricultural economy.
-
A.
Dunaharaszti
Dunaharaszti is a town in central Hungary that functions largely as a suburban residential and industrial area near Budapest.
-
B.
Bicske
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
-
C.
Bodrogköz
Bodrogköz is a low-lying, marshy region in northeastern Hungary known for its riverine landscapes, wetlands, and traditional rural settlements.
-
D.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
E.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
- 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3147ecc81908cfca84c05a367d9 |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23ceca020819080f310f84669551a |
completed | April 5, 2026, 10:43 a.m. |
| NEDg | Description generation | batch_69d23e6ca3908190b7ad7b932ab35ad7 |
completed | April 5, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d241020074819092bc2deea85a6ac0 |
completed | April 5, 2026, 11:01 a.m. |
Created at: March 30, 2026, 8:31 p.m.