Triple
T9632186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Francia |
E232833
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Szeged, Hungary |
E37566
|
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: Szeged, Hungary | Statement: [Susan Francia, placeOfBirth, Szeged, Hungary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szeged, Hungary Context triple: [Susan Francia, placeOfBirth, Szeged, Hungary]
-
A.
Szeged
chosen
Szeged is a prominent city in southern Hungary known for its university, paprika production, and distinctive Art Nouveau architecture.
-
B.
Kaposvár, Hungary
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
C.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
D.
Budaörs, Hungary
Budaörs is a suburban town just west of Budapest in Hungary, known for its rapid post-communist development, commercial centers, and role as a key transport hub near the capital.
-
E.
Kisvárda, Hungary
Kisvárda is a small town in northeastern Hungary known for its historic castle, thermal baths, and role as a regional cultural and economic center.
- 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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b2783b48190a9929dc3e3cd2956 |
completed | April 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d597224481908154e5db47ae52f9 |
completed | April 5, 2026, 3:23 a.m. |
Created at: March 30, 2026, 8:11 p.m.