Triple
T15293078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapos River |
E365576
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Tolna County |
E236810
|
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: Tolna County | Statement: [Kapos River, region, Tolna County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tolna County Context triple: [Kapos River, region, Tolna County]
-
A.
Tolna County
chosen
Tolna County is an administrative region in central Hungary known for its agricultural landscape and location along the Danube River.
-
B.
Somogy County
Somogy County is an administrative region in southwestern Hungary, known for its rural landscapes and proximity to Lake Balaton.
-
C.
Zólyom County
Zólyom County was a historic administrative county of the Kingdom of Hungary, located in what is now central Slovakia around the town of Zvolen.
-
D.
Nógrád County
Nógrád County is a northern Hungarian administrative region known for its hilly landscapes, historic towns, and portions of the Mátra and Cserhát mountain ranges.
-
E.
Gömör-Kishont County
Gömör-Kishont County was a historic administrative county of the Kingdom of Hungary, located in what is today southern Slovakia and northern Hungary.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03682ea488190ac82fdbd0e855d34 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b3b8a8c81908aa936613565b87a |
completed | May 9, 2026, 10:23 a.m. |
Created at: April 10, 2026, 3:15 a.m.