Triple
T10092759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Hungary |
E215782
|
entity |
| Predicate | containsSpaTown |
P19664
|
FINISHED |
| Object |
Egerszalók
Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
|
E959078
|
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: Egerszalók | Statement: [Northern Hungary, containsSpaTown, Egerszalók]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Egerszalók Context triple: [Northern Hungary, containsSpaTown, Egerszalók]
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
C.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
D.
Hajdúszoboszló
Hajdúszoboszló is a Hungarian spa town renowned for its thermal baths and large water park, making it a major health and wellness tourism destination.
-
E.
Dunakeszi
Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
- 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: Egerszalók Triple: [Northern Hungary, containsSpaTown, Egerszalók]
Generated description
Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Egerszalók Target entity description: Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
C.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
D.
Hajdúszoboszló
Hajdúszoboszló is a Hungarian spa town renowned for its thermal baths and large water park, making it a major health and wellness tourism destination.
-
E.
Dunakeszi
Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
- 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_69ca83a4947c8190823a7495dc5d96ed |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd05c3c0c8190927580717429a4e5 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f489b62b808190a3fc89e73e8ae2f7 |
completed | May 1, 2026, 11:08 a.m. |
| NEDg | Description generation | batch_69f48df7790c8190a8e79466f032e86d |
completed | May 1, 2026, 11:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f490dfe4e4819092d7494ba9b807db |
completed | May 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 9:01 p.m.