Triple
T17616882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kőszeg |
E429106
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Vas County |
—
|
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: Vas County | Statement: [Kőszeg, locatedIn, Vas County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vas County Context triple: [Kőszeg, locatedIn, Vas County]
-
A.
Vas County
chosen
Vas County is an administrative region in western Hungary known for its historic towns, thermal spas, and proximity to the Austrian and Slovenian borders.
-
B.
McKenzie County
McKenzie County is a sparsely populated county in western North Dakota known for its oil production, ranching, and access to outdoor recreation along Lake Sakakawea and the Badlands.
-
C.
Manas County
Manas County is an administrative division in Xinjiang, China, known for its agricultural production and proximity to Manas Lake.
-
D.
Tandora County
Tandora County is a cadastral land division in New South Wales, Australia, used primarily for property and land title purposes.
-
E.
Dawson County
Dawson County is a rural county in the western part of Texas known for its agriculture and oil production.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d32991c81909801161b0a416c94 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.