Triple
T16280012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Hungary |
E395238
|
entity |
| Predicate | partlyLocatedIn |
P40
|
FINISHED |
| Object | Alföld |
E566015
|
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: Alföld | Statement: [Southern Hungary, partlyLocatedIn, Alföld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alföld Context triple: [Southern Hungary, partlyLocatedIn, Alföld]
-
A.
Great Hungarian Plain
chosen
The Great Hungarian Plain is a vast lowland region in eastern and southern Hungary known for its flat landscapes, agriculture, and traditional rural culture.
-
B.
Erdő
Erdő is a Hungarian surname most notably borne by Péter Erdő, a prominent Hungarian cardinal and former Primate of Hungary.
-
C.
Little Hungarian Plain
The Little Hungarian Plain is a low-lying agricultural region in northwestern Hungary known for its fertile soils and flat landscape.
-
D.
Kelenföld
Kelenföld is a residential and transport hub neighborhood in southwestern Budapest, known for its major railway and metro interchange and large housing estates.
-
E.
Upland Hungary
Upland Hungary is a historical term referring to the northern, upland regions of the former Kingdom of Hungary, much of which lies in present-day Slovakia.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24611926c81909b276ca3f406f15d |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025fb88488190a2979c75fba67b9d |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:05 a.m.