Triple
T17014057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rába |
E412771
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Sárvár |
—
|
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: Sárvár | Statement: [Rába, flowsThroughCity, Sárvár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sárvár Context triple: [Rába, flowsThroughCity, Sárvár]
-
A.
Sárvár
chosen
Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
-
B.
Vasvár
Vasvár is a small historic town in western Hungary known for its medieval heritage and role as a former county seat.
-
C.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
D.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
E.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d47e64f081908f43870c7564d0ae |
completed | April 18, 2026, 6:59 p.m. |
Created at: April 10, 2026, 5:33 a.m.