Triple
T8018102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sümeg Castle |
E186670
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Sümeg |
E186670
|
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: Sümeg | Statement: [Sümeg Castle, locatedIn, Sümeg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sümeg Context triple: [Sümeg Castle, locatedIn, Sümeg]
-
A.
Sümeg
chosen
Sümeg is a small historic town in western Hungary, best known for its well-preserved medieval hilltop castle and baroque architecture.
-
B.
Somlyó
Somlyó is a historical locality in the Kingdom of Hungary, best known as the birthplace of Stephen Báthory, who became King of Poland and Grand Duke of Lithuania in the 16th century.
-
C.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
-
D.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
-
E.
Sárbogárd
Sárbogárd is a small town in central Hungary known for its agricultural surroundings and role as a local transport hub within Fejér County.
- 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_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3df626e8819098a9f8908dfdad3b |
completed | March 31, 2026, 3:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc56c213ec8190b3bd96c42d1357e4 |
completed | March 31, 2026, 11:20 p.m. |
Created at: March 30, 2026, 5:20 p.m.