Triple
T1404831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Balaton |
E31666
|
entity |
| Predicate | hasResortTown |
P847
|
FINISHED |
| Object | Keszthely |
E168264
|
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: Keszthely | Statement: [Lake Balaton, hasResortTown, Keszthely]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keszthely Context triple: [Lake Balaton, hasResortTown, Keszthely]
-
A.
Keszthely
chosen
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
-
B.
Komló
Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
-
C.
Kecskemét
Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
-
D.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
-
E.
Tatabánya
Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c48ff58c8190aeaf09d3e7cad7c7 |
completed | March 1, 2026, 10:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad293cb8f0819085bea7914abf0683 |
completed | March 8, 2026, 7:46 a.m. |
Created at: March 1, 2026, 7:59 p.m.