Triple
T6540177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keszthely |
E168264
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Hévíz Lake |
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 Lake | Statement: [Keszthely, locatedNear, Hévíz Lake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hévíz Lake Context triple: [Keszthely, locatedNear, Hévíz Lake]
-
A.
Lake Balaton
Lake Balaton is a major Central European freshwater lake in western Hungary, renowned as a popular tourist and recreation destination.
-
B.
Hévíz
chosen
Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
-
C.
Ségny
Ségny is a small commune in eastern France’s Ain department, situated near the Swiss border in the Auvergne-Rhône-Alpes region.
-
D.
Bodrog
Bodrog is a river in Central Europe that flows through Slovakia and Hungary before joining the Tisza River.
-
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.
- 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_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_69c6eed36a7081909cb70b79f18b0dfc |
completed | March 27, 2026, 8:55 p.m. |
Created at: March 27, 2026, 1:50 p.m.