Triple
T14310159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kőbánya-Kispest |
E354805
|
entity |
| Predicate | locatedInDistrict |
P40
|
FINISHED |
| Object | Kőbánya |
E354805
|
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: Kőbánya | Statement: [Kőbánya-Kispest, locatedInDistrict, Kőbánya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kőbánya Context triple: [Kőbánya-Kispest, locatedInDistrict, Kőbánya]
-
A.
Besztercebánya
Besztercebánya is a historic mining and cultural city in central Slovakia, known in Hungarian as Besztercebánya and in Slovak as Banská Bystrica.
-
B.
Selmecbánya
Selmecbánya is the historical Hungarian name for Banská Štiavnica, a renowned medieval mining town and UNESCO World Heritage site in present-day central Slovakia.
-
C.
Kékes
Kékes is the highest peak in Hungary, known for its popular hiking trails and ski resort facilities.
-
D.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
-
E.
Kőbánya-Kispest
chosen
Kőbánya-Kispest is a major transport hub in Budapest that serves as the southeastern terminus of Metro Line M3 and connects metro, suburban rail, and numerous bus services.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b26da48190a96e2f60ace51335 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4684e2648190b46328252ac9d51b |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:12 a.m.