Triple
T9397870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carl Flesch |
E226391
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Mosonmagyaróvár |
E663578
|
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: Mosonmagyaróvár | Statement: [Carl Flesch, placeOfBirth, Mosonmagyaróvár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mosonmagyaróvár Context triple: [Carl Flesch, placeOfBirth, Mosonmagyaróvár]
-
A.
Mosonmagyaróvár
chosen
Mosonmagyaróvár is a historic town in northwestern Hungary near the Austrian and Slovak borders, known for its thermal baths and strategic location as a regional transport hub.
-
B.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
C.
Mosonszentmiklós
Mosonszentmiklós is a village in northwestern Hungary, notable as the birthplace of renowned conductor Arthur Nikisch.
-
D.
Sárvár
Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
-
E.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
- 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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51541020819097da2eb60be73760 |
completed | April 1, 2026, 5:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1011aef64819085cbb7e04c2d87b2 |
completed | April 4, 2026, 12:16 p.m. |
Created at: March 30, 2026, 7:46 p.m.