Triple
T7608257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hungarian Sea |
E180163
|
entity |
| Predicate | associatedWithCity |
P1481
|
FINISHED |
| Object | Balatonfüred |
E166290
|
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: Balatonfüred | Statement: [Hungarian Sea, associatedWithCity, Balatonfüred]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balatonfüred Context triple: [Hungarian Sea, associatedWithCity, Balatonfüred]
-
A.
Balatonfüred
chosen
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.
-
B.
Balatonfűzfő
Balatonfűzfő is a small Hungarian town on the northern shore of Lake Balaton, known for its lakeside recreation and industrial history.
-
C.
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.
-
D.
Balatonalmádi
Balatonalmádi is a popular Hungarian resort town on the northern shore of Lake Balaton, known for its beaches, holiday facilities, and scenic surroundings.
-
E.
Hévíz
Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6fa1de8a4819091f9e9347835ce16 |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8685c050c8190b05fa19c9ae2c827 |
completed | March 28, 2026, 11:46 p.m. |
Created at: March 27, 2026, 3:54 p.m.