Triple
T10012490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baranya County |
E199407
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Pécsvárad |
E590458
|
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: Pécsvárad | Statement: [Baranya County, containsTown, Pécsvárad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pécsvárad Context triple: [Baranya County, containsTown, Pécsvárad]
-
A.
Pécsvárad
chosen
Pécsvárad is a small historic town in southern Hungary known for its medieval abbey and scenic setting near the Mecsek Mountains.
-
B.
Gyulafehérvár
Gyulafehérvár, known today as Alba Iulia in Romania, is a historic city that served as the political and cultural center of Transylvania for centuries.
-
C.
Tiszaföldvár
Tiszaföldvár is a small town in eastern Hungary known for its agricultural surroundings and location near the Tisza River.
-
D.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd3cf5b881908f5318e55bdd22b6 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f018324bf88190bcd2bf168b1065d3 |
completed | April 28, 2026, 2:15 a.m. |
Created at: March 30, 2026, 8:52 p.m.