Triple
T8352663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zsolnay Cultural Quarter |
E196598
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | City of Pécs |
E38480
|
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: City of Pécs | Statement: [Zsolnay Cultural Quarter, ownedBy, City of Pécs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Pécs Context triple: [Zsolnay Cultural Quarter, ownedBy, City of Pécs]
-
A.
Pécs
chosen
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.
-
B.
Makó
Makó is a town in southeastern Hungary, renowned for its onion production and thermal baths.
-
C.
Gödöllő
Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
-
D.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
-
E.
Kaposvár, Hungary
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
- 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_69ca82f08b348190bfb7881944bbff6f |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80460f048190aa298ddffde1047d |
completed | March 31, 2026, 8:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1cfe284081909410e023c44c7472 |
completed | April 2, 2026, 7:38 a.m. |
Created at: March 30, 2026, 5:59 p.m.