Triple
T4675637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alba Iulia |
E103673
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Gyulafehérvár |
E450832
|
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: Gyulafehérvár | Statement: [Alba Iulia, formerName, Gyulafehérvár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyulafehérvár Context triple: [Alba Iulia, formerName, Gyulafehérvár]
-
A.
Gyulafehérvár
chosen
Gyulafehérvár, known today as Alba Iulia in Romania, is a historic city that served as the political and cultural center of Transylvania for centuries.
-
B.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
C.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
-
D.
Székesfehérvár
Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
-
E.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
- 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_69bd43dda32c8190938b37744ca270fc |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd63544f68819084311416402696b0 |
completed | March 20, 2026, 3:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4399ded08190ba97c9c2f98395ec |
completed | March 21, 2026, 7:07 a.m. |
Created at: March 20, 2026, 1:16 p.m.