Triple
T15049832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Walpurga |
E379329
|
entity |
| Predicate | patronage |
P2320
|
FINISHED |
| Object | Eichstätt |
E680543
|
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: Eichstätt | Statement: [Saint Walpurga, patronage, Eichstätt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eichstätt Context triple: [Saint Walpurga, patronage, Eichstätt]
-
A.
Eichstätt
chosen
Eichstätt is a historic Bavarian town in southern Germany known for its baroque architecture, Catholic university, and location within the Altmühltal Nature Park.
-
B.
Rosenheim
Rosenheim is a town in Upper Bavaria, Germany, known as a regional economic and transportation hub near the Alps.
-
C.
Traunstein
Traunstein is a town in southeastern Bavaria, Germany, known as a regional administrative and cultural center near the Chiemsee and the Alps.
-
D.
Kaufbeuren
Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
-
E.
Straubing
Straubing is a Bavarian town on the Danube River known for its historic city center and role as a regional economic and educational hub.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda8f71988190b4fe7f7de4ccb798 |
completed | April 15, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00baf8a2648190adf3ad3af118187f |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 3 a.m.