Triple
T17243549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zala County |
E418562
|
entity |
| Predicate | hasThermalSpaTown |
P19664
|
FINISHED |
| Object | Hévíz |
E604157
|
NE FINISHED |
How this triple was built (2 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: [Zala County, hasThermalSpaTown, Hévíz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hévíz Context triple: [Zala County, hasThermalSpaTown, Hévíz]
-
A.
Hévíz
chosen
Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
-
B.
Sárospatak
Sárospatak is a historic town in northeastern Hungary, renowned for its medieval castle and role as a cultural and educational center in the region.
-
C.
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.
-
D.
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.
-
E.
Bóly
Bóly is a small town in southern Hungary known for its agricultural surroundings and location within Baranya County.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e21bb5c8190ad960f231fe54665 |
completed | April 19, 2026, 1:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170f388608190b709b1c228a7ba29 |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:39 a.m.