Triple
T9703786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aba |
E234844
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Fejér County |
E32753
|
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: Fejér County | Statement: [Aba, partOf, Fejér County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fejér County Context triple: [Aba, partOf, Fejér County]
-
A.
Fejér County
chosen
Fejér County is an administrative region in central Hungary known for its historical significance and industrial centers, with Székesfehérvár as its county seat.
-
B.
Somogy County
Somogy County is an administrative region in southwestern Hungary, known for its rural landscapes and proximity to Lake Balaton.
-
C.
Nógrád County
Nógrád County is a northern Hungarian administrative region known for its hilly landscapes, historic towns, and portions of the Mátra and Cserhát mountain ranges.
-
D.
Tolna County
Tolna County is an administrative region in central Hungary known for its agricultural landscape and location along the Danube River.
-
E.
Liptó County
Liptó County was a historic administrative county of the Kingdom of Hungary, located in the northern part of present-day Slovakia and centered around the Liptov region.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d73a0148190ad4178fd462cdd9c |
completed | April 1, 2026, 10:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d269858f648190a9c3b730d37ecf9c |
completed | April 5, 2026, 1:54 p.m. |
Created at: March 30, 2026, 8:18 p.m.