Triple
T12754435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lučenec |
E304819
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Karcag |
E687173
|
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: Karcag | Statement: [Lučenec, hasTwinTown, Karcag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karcag Context triple: [Lučenec, hasTwinTown, Karcag]
-
A.
Karcag
chosen
Karcag is a town in eastern Hungary known for its Great Hungarian Plain agricultural traditions and historic Calvinist heritage.
-
B.
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.
-
C.
Kiskőrös
Kiskőrös is a small town in southern Hungary known as the birthplace of the national poet Sándor Petőfi and for its wine-producing region.
-
D.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
E.
Kőszeg
Kőszeg is a historic Hungarian town near the Austrian border, renowned for its well-preserved medieval architecture and role in defending against Ottoman sieges.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd084d94081909ae911fce5640aaf |
completed | May 7, 2026, 5:48 p.m. |
Created at: April 9, 2026, 5:27 p.m.