Triple
T16280001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Hungary |
E395238
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Szekszárd |
E587450
|
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: Szekszárd | Statement: [Southern Hungary, hasMajorCity, Szekszárd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szekszárd Context triple: [Southern Hungary, hasMajorCity, Szekszárd]
-
A.
Szekszárd
chosen
Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
-
B.
Szekesfehervar
Szekesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
-
C.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
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.
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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24611926c81909b276ca3f406f15d |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a014134601c81909f7f4a95d558e067 |
completed | May 11, 2026, 2:38 a.m. |
Created at: April 10, 2026, 5:05 a.m.