Triple
T843267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Felix Unger |
E18221
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Salzburg |
E19756
|
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: Salzburg | Statement: [Felix Unger, workLocation, Salzburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salzburg Context triple: [Felix Unger, workLocation, Salzburg]
-
A.
Salzburg
chosen
Salzburg is a historic Austrian city on the Salzach River, renowned for its baroque architecture, Alpine setting, and as the birthplace of composer Wolfgang Amadeus Mozart.
-
B.
Innsbruck
Innsbruck is a city in western Austria known for its Alpine setting and winter sports facilities, and it later successfully hosted the Winter Olympics in 1964 and 1976.
-
C.
Linz
Linz is a major Austrian city known for its industrial heritage, vibrant cultural scene, and location along the Danube River.
-
D.
Vienne
Vienne is a historic town in southeastern France known for its well-preserved Roman and medieval heritage, including ancient temples, a Roman theater, and a Gothic cathedral.
-
E.
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.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abe8a0bc81909b54af465e67be1f |
completed | March 1, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4bfac5cc8190a5fba1c5da98391d |
completed | March 7, 2026, 4:02 p.m. |
Created at: March 1, 2026, 7:38 p.m.