Triple
T19512003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bakony Mountains |
E488178
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Várpalota |
—
|
NE NERFINISHED |
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: Várpalota | Statement: [Bakony Mountains, nearCity, Várpalota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Várpalota Context triple: [Bakony Mountains, nearCity, Várpalota]
-
A.
Várpalota
chosen
Várpalota is a town in western Hungary known for its historical castle and industrial heritage.
-
B.
Tiszaföldvár
Tiszaföldvár is a small town in eastern Hungary known for its agricultural surroundings and location near the Tisza River.
-
C.
Városmajor
Városmajor is a park-filled neighborhood in Budapest known for its green spaces, recreational areas, and role as a local transport hub.
-
D.
Oroszvár
Oroszvár is a historic locality in present-day western Slovakia (now part of Rusovce, a borough of Bratislava) known in part as a former residence of Princess Louise of Belgium.
-
E.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359908fc8190bd05f26d4271d268 |
completed | April 20, 2026, 2:18 p.m. |
Created at: April 10, 2026, 1:40 p.m.