Triple
T5334207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Veszprém County |
E123785
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Sümeg |
E186670
|
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: Sümeg | Statement: [Veszprém County, containsTown, Sümeg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sümeg Context triple: [Veszprém County, containsTown, Sümeg]
-
A.
Sümeg
chosen
Sümeg is a small historic town in western Hungary, best known for its well-preserved medieval hilltop castle and baroque architecture.
-
B.
Somlyó
Somlyó is a historical locality in the Kingdom of Hungary, best known as the birthplace of Stephen Báthory, who became King of Poland and Grand Duke of Lithuania in the 16th century.
-
C.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
-
D.
Sárbogárd
Sárbogárd is a small town in central Hungary known for its agricultural surroundings and role as a local transport hub within Fejér County.
-
E.
Zamárdi
Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
- 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_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85ae52c08190968a5567b7e6b794 |
completed | March 20, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf18baeca081909acc11d0c6c89f6d |
completed | March 21, 2026, 10:16 p.m. |
Created at: March 20, 2026, 2 p.m.