Triple
T17249146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiskunfélegyháza |
E418702
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object |
Kiskunság
Kiskunság is a historical and geographical region in central Hungary characterized by its sandy plains, steppe landscapes, and traditional rural settlements.
|
E1258666
|
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: Kiskunság | Statement: [Kiskunfélegyháza, region, Kiskunság]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiskunság Context triple: [Kiskunfélegyháza, region, Kiskunság]
-
A.
Mundruczó
Mundruczó is the surname of Hungarian film and theatre director Kornél Mundruczó, known for his innovative and often provocative works.
-
B.
Zagyva
Zagyva is a river in northern Hungary that flows through towns such as Salgótarján and Hatvan before joining the Tisza River.
-
C.
Karcsag
Karcsag is a town in eastern Hungary known as the birthplace of Nobel Prize–winning biochemist Avram Hershko.
-
D.
Tarnazsadány
Tarnazsadány is a small village in northern Hungary, situated within Heves County and characterized by its rural, agricultural setting.
-
E.
Piliscsaba
Piliscsaba is a town in Hungary known for its scenic setting near the Pilis Mountains and its role as a local educational and cultural center.
- 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: Kiskunság Triple: [Kiskunfélegyháza, region, Kiskunság]
Generated description
Kiskunság is a historical and geographical region in central Hungary characterized by its sandy plains, steppe landscapes, and traditional rural settlements.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kiskunság Target entity description: Kiskunság is a historical and geographical region in central Hungary characterized by its sandy plains, steppe landscapes, and traditional rural settlements.
-
A.
Mundruczó
Mundruczó is the surname of Hungarian film and theatre director Kornél Mundruczó, known for his innovative and often provocative works.
-
B.
Zagyva
Zagyva is a river in northern Hungary that flows through towns such as Salgótarján and Hatvan before joining the Tisza River.
-
C.
Karcsag
Karcsag is a town in eastern Hungary known as the birthplace of Nobel Prize–winning biochemist Avram Hershko.
-
D.
Tarnazsadány
Tarnazsadány is a small village in northern Hungary, situated within Heves County and characterized by its rural, agricultural setting.
-
E.
Piliscsaba
Piliscsaba is a town in Hungary known for its scenic setting near the Pilis Mountains and its role as a local educational and cultural center.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e2636f48190b29548ff80402bef |
completed | April 19, 2026, 1:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170f96e548190be92846e072118f9 |
completed | May 11, 2026, 6:02 a.m. |
| NEDg | Description generation | batch_6a01717495208190b415219d15e71fb7 |
completed | May 11, 2026, 6:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01721f5b9081909a8bc817ba0a5986 |
completed | May 11, 2026, 6:07 a.m. |
Created at: April 10, 2026, 5:39 a.m.