Triple
T14865004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budapest I. kerülete |
E349593
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Krisztinaváros |
E349576
|
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: Krisztinaváros | Statement: [Budapest I. kerülete, contains, Krisztinaváros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krisztinaváros Context triple: [Budapest I. kerülete, contains, Krisztinaváros]
-
A.
Krisztinaváros
chosen
Krisztinaváros is a historic neighborhood in Budapest’s Buda side, known for its 19th-century architecture, cultural landmarks, and proximity to the Castle District.
-
B.
Józsiváros
Józsiváros is a colloquial nickname for Budapest’s 8th district, Józsefváros, often used informally by locals.
-
C.
Dunakeszi
Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
-
D.
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.
-
E.
Budaörs
Budaörs is a suburban town near Budapest in Hungary, known for its rapid post-communist development and role as a commercial and residential 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5761c688190b4477cb081554b51 |
completed | April 15, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff45406c8c8190beb87d4bb5c50355 |
completed | May 9, 2026, 2:31 p.m. |
Created at: April 10, 2026, 1:55 a.m.