Triple
T19512068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunántúl |
E488179
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Keszthely |
—
|
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: Keszthely | Statement: [Dunántúl, containsCity, Keszthely]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keszthely Context triple: [Dunántúl, containsCity, Keszthely]
-
A.
Keszthely
chosen
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
-
B.
Tiszakécske
Tiszakécske is a town in central Hungary known for its location along the Tisza River and its thermal baths.
-
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.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
E.
Tihany
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
- 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359908fc8190bd05f26d4271d268 |
completed | April 20, 2026, 2:18 p.m. |
Created at: April 10, 2026, 1:40 p.m.