Triple
T7737030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esztergom |
E175407
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Mariazell |
E344008
|
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: Mariazell | Statement: [Esztergom, hasTwinTown, Mariazell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariazell Context triple: [Esztergom, hasTwinTown, Mariazell]
-
A.
Mariazell
chosen
Mariazell is a renowned Austrian pilgrimage town in Styria, famous for its basilica and long-standing Catholic religious traditions.
-
B.
Klosterneuburg
Klosterneuburg is an Austrian town near Vienna, known for its historic Augustinian monastery and wine-growing tradition along the Danube River.
-
C.
Graz
Graz is Austria’s second-largest city, known for its well-preserved medieval old town and historic role as a center of science and education.
-
D.
Leoben
Leoben is a historic industrial and university city in the Austrian state of Styria, known especially for its steel industry and mining university.
-
E.
Gmunden
Gmunden is a picturesque town in Upper Austria known for its lakeside setting on the Traunsee and its historic ceramics industry.
- 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_69c6995f9c60819092e386192bd63c6f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7035923108190842025631e2314cc |
completed | March 27, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be3958ac8190a48ba07bd8ea3251 |
completed | March 29, 2026, 5:52 a.m. |
Created at: March 27, 2026, 4:07 p.m.