Triple
T14059751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heves County |
E338311
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mátraháza |
E343064
|
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: Mátraháza | Statement: [Heves County, contains, Mátraháza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mátraháza Context triple: [Heves County, contains, Mátraháza]
-
A.
Mátraháza
chosen
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.
-
B.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
C.
Bonyhád
Bonyhád is a town in southern Hungary known as an important local center within Tolna County.
-
D.
Balvanyos
Balvanyos is a Romanian mountain resort area known for its natural mineral springs, spa facilities, and scenic surroundings in the Eastern Carpathians.
-
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 (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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5686f51c81908c33143ecbaae83d |
completed | April 14, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe64e47f1c8190a4ad09bc96d35b69 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 9, 2026, 10:21 p.m.