Triple
T13713591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schengen |
E328834
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Sátoraljaújhely |
E214382
|
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: Sátoraljaújhely | Statement: [Schengen, hasTwinTown, Sátoraljaújhely]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sátoraljaújhely Context triple: [Schengen, hasTwinTown, Sátoraljaújhely]
-
A.
Sátoraljaújhely
chosen
Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
-
B.
Kalocsa
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.
-
C.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
D.
Hajdúszoboszló
Hajdúszoboszló is a Hungarian spa town renowned for its thermal baths and large water park, making it a major health and wellness tourism destination.
-
E.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a5a733c819090a6710ab990c38d |
completed | May 9, 2026, 11:28 a.m. |
Created at: April 9, 2026, 9:54 p.m.