Triple
T20821356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satu Mare County |
E512579
|
entity |
| Predicate | locatedInHistoricalRegion |
P915
|
FINISHED |
| Object | Sătmar |
—
|
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: Sătmar | Statement: [Satu Mare County, locatedInHistoricalRegion, Sătmar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sătmar Context triple: [Satu Mare County, locatedInHistoricalRegion, Sătmar]
-
A.
Szatmár
chosen
Szatmár is the Hungarian name for Satu Mare, a historic city in northwestern Romania near the Hungarian border.
-
B.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
-
C.
Egerszalók
Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
-
D.
Derecske
Derecske is a small town in eastern Hungary located within Hajdú-Bihar County, known for its agricultural surroundings and local rural character.
-
E.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
- 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_69e0b4ce39108190a6e8e5df4f1c8dc5 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f7a1548190b6ef3f1cfad37c1c |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.