Triple
T10092757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Hungary |
E215782
|
entity |
| Predicate | hasHighestPeak |
P1674
|
FINISHED |
| Object | Kékes |
E338310
|
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: Kékes | Statement: [Northern Hungary, hasHighestPeak, Kékes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kékes Context triple: [Northern Hungary, hasHighestPeak, Kékes]
-
A.
Kékes
chosen
Kékes is the highest peak in Hungary, known for its popular hiking trails and ski resort facilities.
-
B.
Kunhegyes
Kunhegyes is a small town in Jász-Nagykun-Szolnok County in central Hungary, known for its rural character and agricultural surroundings.
-
C.
Nagyerdő
Nagyerdő is a large, historic forested park and recreational area in Debrecen, Hungary, known for its natural beauty and cultural attractions.
-
D.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
E.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain 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_69ca83a4947c8190823a7495dc5d96ed |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd05c3c0c8190927580717429a4e5 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b6aecc488190a9af098f687327ed |
completed | April 5, 2026, 7:23 p.m. |
Created at: March 30, 2026, 9:01 p.m.