Triple
T7000018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tihany Peninsula |
E162312
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Tihany |
E518226
|
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: [Tihany Peninsula, hasSettlement, Tihany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tihany Context triple: [Tihany Peninsula, hasSettlement, Tihany]
-
A.
Tihany
chosen
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
-
B.
Devecser
Devecser is a small town in western Hungary known for its location in Veszprém County and for being affected by the 2010 Ajka alumina plant red sludge disaster.
-
C.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
-
D.
Hévíz
Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
-
E.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
- 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc0e54c88190b092870f2d128510 |
completed | March 27, 2026, 7:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7884537708190a35d6988b1fa9b15 |
completed | March 28, 2026, 7:50 a.m. |
Created at: March 27, 2026, 2:33 p.m.