Triple
T6540176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keszthely |
E168264
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Hévíz
Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
|
E604157
|
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: Hévíz | Statement: [Keszthely, locatedNear, Hévíz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hévíz Context triple: [Keszthely, locatedNear, Hévíz]
-
A.
Tihany
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
-
B.
Balatonfüred
Balatonfüred is a historic Hungarian resort town and spa destination on the northern shore of Lake Balaton, known for its promenades, sailing, and mineral springs.
-
C.
Devecser
Devecser is a small town in western Hungary known for its location in Veszprém County and for being affected by the 2010 Ajka alumina plant red sludge disaster.
-
D.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
-
E.
Balatonlelle
Balatonlelle is a popular Hungarian holiday town on the southern shore of Lake Balaton, known for its beaches, family-friendly attractions, and lakeside resorts.
- 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: Hévíz Triple: [Keszthely, locatedNear, Hévíz]
Generated description
Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hévíz Target entity description: Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
-
A.
Tihany
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
-
B.
Balatonfüred
Balatonfüred is a historic Hungarian resort town and spa destination on the northern shore of Lake Balaton, known for its promenades, sailing, and mineral springs.
-
C.
Devecser
Devecser is a small town in western Hungary known for its location in Veszprém County and for being affected by the 2010 Ajka alumina plant red sludge disaster.
-
D.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
-
E.
Balatonlelle
Balatonlelle is a popular Hungarian holiday town on the southern shore of Lake Balaton, known for its beaches, family-friendly attractions, and lakeside resorts.
- 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_69c68a51564081909e93aee0dbd9cca3 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6add5d3848190a0d70dc4013ab756 |
completed | March 27, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d53b861c81908adc984a3067d4ef |
completed | March 27, 2026, 7:06 p.m. |
| NEDg | Description generation | batch_69c6d6745b40819083fbcb2a4063e34d |
completed | March 27, 2026, 7:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6d837a5248190b0afb39174ac3922 |
completed | March 27, 2026, 7:19 p.m. |
Created at: March 27, 2026, 1:50 p.m.