Triple
T4875580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tisa River |
E109194
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Szolnok |
E284469
|
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: Szolnok | Statement: [Tisa River, flowsThroughCity, Szolnok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szolnok Context triple: [Tisa River, flowsThroughCity, Szolnok]
-
A.
Szolnok
chosen
Szolnok is a city in central Hungary known as an important regional industrial and transportation hub along the Tisza River.
-
B.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
C.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
D.
Szombathely
Szombathely is one of Hungary’s oldest cities, known for its Roman heritage and role as a regional cultural and economic center near the Austrian border.
-
E.
Miskolc
Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
- 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_69be6fb5da2c8190aeec7d6d11b12b11 |
completed | March 21, 2026, 10:15 a.m. |
Created at: March 20, 2026, 1:27 p.m.