Triple
T4875579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tisa River |
E109194
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Tokaj |
E234399
|
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: Tokaj | Statement: [Tisa River, flowsThroughCity, Tokaj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokaj Context triple: [Tisa River, flowsThroughCity, Tokaj]
-
A.
Tokaj
chosen
Tokaj is a historic town in northeastern Hungary renowned worldwide for its Tokaji wine region and sweet dessert wines.
-
B.
Sopron wine region
Sopron wine region is a historic Hungarian wine-producing area near the Austrian border, known especially for its Kékfrankos (Blaufränkisch) red wines.
-
C.
Makó
Makó is a town in southeastern Hungary, renowned for its onion production and thermal baths.
-
D.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
E.
Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
- 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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6dba3efc8190adcf8b30490b4984 |
completed | March 20, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be67f90e848190a36eee1e670657e4 |
completed | March 21, 2026, 9:42 a.m. |
Created at: March 20, 2026, 1:27 p.m.