Triple
T16279999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Hungary |
E395238
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Kecskemét |
E135511
|
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: Kecskemét | Statement: [Southern Hungary, hasMajorCity, Kecskemét]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kecskemét Context triple: [Southern Hungary, hasMajorCity, Kecskemét]
-
A.
Kecskemét
chosen
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.
-
B.
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.
-
C.
Miskolc
Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
-
D.
Szekesfehervar
Szekesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
-
E.
Kaposvár
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_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_6a0139e380bc81908452f6e8666f23ad |
completed | May 11, 2026, 2:07 a.m. |
Created at: April 10, 2026, 5:05 a.m.