Triple
T5328928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satu Mare |
E123254
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Nyíregyháza |
E408405
|
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: Nyíregyháza | Statement: [Satu Mare, hasTwinTown, Nyíregyháza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nyíregyháza Context triple: [Satu Mare, hasTwinTown, Nyíregyháza]
-
A.
Nyíregyháza
chosen
Nyíregyháza is a major city in northeastern Hungary known as an important regional economic, cultural, and educational center.
-
B.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
C.
Győr
Győr is a historic city in northwestern Hungary, known as an important regional cultural and economic center at the confluence of the Danube, Rába, and Rábca rivers.
-
D.
Békéscsaba
Békéscsaba is a city in southeastern Hungary known as the administrative center of Békés County and for its cultural and culinary traditions, including its famous sausage.
-
E.
Kecskemét
Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
- 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_69bd46477f9081909d242a327d749466 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8593bd6c8190b2054e548ddf2458 |
completed | March 20, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfa1bee05c81909ec8b823ee1b6a01 |
completed | March 22, 2026, 8:01 a.m. |
Created at: March 20, 2026, 2 p.m.