Triple
T20815990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tapolca District |
E512438
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Káptalantóti
Káptalantóti is a small village in Veszprém County, Hungary, known for its rural charm and proximity to the northern shore of Lake Balaton.
|
E1454474
|
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: Káptalantóti | Statement: [Tapolca District, containsSettlement, Káptalantóti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Káptalantóti Context triple: [Tapolca District, containsSettlement, Káptalantóti]
-
A.
Kisapáti
Kisapáti is a small village in western Hungary, situated near the volcanic hills of the Tapolca Basin in Veszprém County.
-
B.
Tótkomlós
Tótkomlós is a small town in southeastern Hungary known for its agricultural surroundings and traditional rural character.
-
C.
Karcsag
Karcsag is a town in eastern Hungary known as the birthplace of Nobel Prize–winning biochemist Avram Hershko.
-
D.
Palotanegyed
Palotanegyed is a historic inner-city neighborhood of Budapest known for its 19th-century palaces, grand boulevards, and cultural institutions.
-
E.
Hajdúnánás
Hajdúnánás is a town in eastern Hungary known for its agricultural surroundings, thermal baths, and location within the Northern Great Plain region.
- 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: Káptalantóti Triple: [Tapolca District, containsSettlement, Káptalantóti]
Generated description
Káptalantóti is a small village in Veszprém County, Hungary, known for its rural charm and proximity to the northern shore of Lake Balaton.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Káptalantóti Target entity description: Káptalantóti is a small village in Veszprém County, Hungary, known for its rural charm and proximity to the northern shore of Lake Balaton.
-
A.
Kisapáti
Kisapáti is a small village in western Hungary, situated near the volcanic hills of the Tapolca Basin in Veszprém County.
-
B.
Tótkomlós
Tótkomlós is a small town in southeastern Hungary known for its agricultural surroundings and traditional rural character.
-
C.
Kolontár
Kolontár is a small village in Veszprém County in western Hungary, known for being affected by the 2010 Ajka alumina plant red sludge disaster.
-
D.
Karcsag
Karcsag is a town in eastern Hungary known as the birthplace of Nobel Prize–winning biochemist Avram Hershko.
-
E.
Palotanegyed
Palotanegyed is a historic inner-city neighborhood of Budapest known for its 19th-century palaces, grand boulevards, and cultural institutions.
- 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f3473c81908c43a2ec242b1acd |
completed | April 21, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a090af72eec8190bbe94e62e856c3e5 |
completed | May 17, 2026, 12:25 a.m. |
| NEDg | Description generation | batch_6a090ec070f08190bbc2b2e1ece81058 |
completed | May 17, 2026, 12:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a090f33b3ec8190b26976da2e480802 |
completed | May 17, 2026, 12:43 a.m. |
Created at: April 16, 2026, 12:41 p.m.