Triple
T17330790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Sebaldus of Nuremberg |
E420807
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Sankt Sebald |
E1211698
|
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: Sankt Sebald | Statement: [Saint Sebaldus of Nuremberg, alsoKnownAs, Sankt Sebald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sankt Sebald Context triple: [Saint Sebaldus of Nuremberg, alsoKnownAs, Sankt Sebald]
-
A.
Sankt Heinrich
Sankt Heinrich is a small village in Bavaria, Germany, that forms part of the municipality of Münsing near Lake Starnberg.
-
B.
St. Kajetan
St. Kajetan is the common name for the Theatinerkirche, a prominent Baroque Catholic church in Munich, Germany.
-
C.
Sankt Meinolf
Sankt Meinolf is a locality within the municipality of Möhnesee in North Rhine-Westphalia, Germany.
-
D.
Sankt Englmar
Sankt Englmar is a Bavarian spa and holiday resort village in the Bavarian Forest of Germany, known for its outdoor recreation and tourism.
-
E.
Sebalder Altstadt
chosen
Sebalder Altstadt is the historic northern part of Nuremberg’s old town, known for its medieval streets, landmarks like St. Sebaldus Church, and well-preserved traditional architecture.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d5c788819092bdc4d3de0ec958 |
completed | April 19, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c5025d08190ab2581a3b04ae661 |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.