Triple
T21974797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budaörs |
E542675
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Kisújfalu |
—
|
NE NERFINISHED |
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: Kisújfalu | Statement: [Budaörs, hasTwinTown, Kisújfalu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kisújfalu Context triple: [Budaörs, hasTwinTown, Kisújfalu]
-
A.
Kisújfalu
chosen
Kisújfalu is a small village in Hungary known primarily as a local rural community rather than a major urban or tourist center.
-
B.
Kisújszállás
Kisújszállás is a small town in eastern Hungary known for its agricultural surroundings and location on the Great Hungarian Plain.
-
C.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
D.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
E.
Köveskál
Köveskál is a small village in Hungary’s Veszprém County, known for its picturesque setting near Lake Balaton and its role in the Balaton Uplands wine and tourism region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c48070988190909db97667b9a0ac |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12487a1a88190abb8a51fcd533b6a |
completed | April 28, 2026, 9:20 p.m. |
Created at: April 16, 2026, 8:03 p.m.