Triple
T15401645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ugrin Csák |
E368331
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Kalocsa |
E902571
|
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: Kalocsa | Statement: [Ugrin Csák, workLocation, Kalocsa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalocsa Context triple: [Ugrin Csák, workLocation, Kalocsa]
-
A.
Kalocsa
chosen
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
B.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
C.
Sátoraljaújhely
Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
-
D.
Budakeszi
Budakeszi is a small town in Hungary, located just west of Budapest and known for its surrounding forests and natural recreational areas.
-
E.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8ea0ac8190a5c68b1951ad3db1 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a1d8e088190a2168952ab5dc687 |
completed | May 10, 2026, 1:37 p.m. |
Created at: April 10, 2026, 3:19 a.m.