Triple
T4144037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | southern Pannonian Plain |
E89340
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Baranya |
E199407
|
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: Baranya | Statement: [southern Pannonian Plain, hasPart, Baranya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baranya Context triple: [southern Pannonian Plain, hasPart, Baranya]
-
A.
Makó
Makó is a town in southeastern Hungary, renowned for its onion production and thermal baths.
-
B.
Baranya County
chosen
Baranya County is an administrative region in southern Hungary known for its cultural center Pécs, wine-producing areas, and diverse natural landscapes.
-
C.
Pécs
Pécs is a historic cultural and university city in southwestern Hungary, renowned for its Roman and Ottoman heritage and its designation as a European Capital of Culture in 2010.
-
D.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
E.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
- 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_69aed95785788190ae75bcf0cd1cafdf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af025d2984819095f299327cc399d5 |
completed | March 9, 2026, 5:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576d2f1788190847d38a384abbe67 |
completed | March 14, 2026, 2:55 p.m. |
Created at: March 9, 2026, 3:43 p.m.