Triple
T1404821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Balaton |
E31666
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Tihany |
E162312
|
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: Tihany | Statement: [Lake Balaton, locatedNear, Tihany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tihany Context triple: [Lake Balaton, locatedNear, Tihany]
-
A.
Keszthely
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.
Tatabánya
Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
-
D.
Tihany Peninsula
chosen
Tihany Peninsula is a historic, scenic promontory jutting into Hungary’s Lake Balaton, known for its volcanic landscape, lavender fields, and the medieval Tihany Abbey.
-
E.
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.
- 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_69a4c3bb3a9c81909db2ad91defd87b6 |
completed | March 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1c97ba748190a227457bcb87a733 |
completed | March 8, 2026, 6:52 a.m. |
Created at: March 1, 2026, 7:59 p.m.