Triple
T4099273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Transdanubia |
E87898
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Sopron |
E99815
|
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: Sopron | Statement: [Western Transdanubia, containsCity, Sopron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sopron Context triple: [Western Transdanubia, containsCity, Sopron]
-
A.
Sopron
chosen
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
-
B.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
C.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
D.
Gödöllő
Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
-
E.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
- 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_69aed94564cc8190a9c1457daedb6e7f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd0bdea48190805a79515ee92709 |
completed | March 9, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589d825c48190b4208b0502c257bb |
completed | March 14, 2026, 4:16 p.m. |
Created at: March 9, 2026, 3:40 p.m.