Triple
T9315287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gisela of Bavaria |
E224102
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Regensburg |
E127596
|
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: Regensburg | Statement: [Gisela of Bavaria, placeOfBirth, Regensburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Regensburg Context triple: [Gisela of Bavaria, placeOfBirth, Regensburg]
-
A.
Regensburg
chosen
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
-
B.
Augsburg
Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
-
C.
Bamberg
Bamberg is a historic city in northern Bavaria, Germany, renowned for its well-preserved medieval old town and status as a UNESCO World Heritage Site.
-
D.
Kaufbeuren
Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
-
E.
Ulm
Ulm is a historic city in the German state of Baden-Württemberg, best known for its towering Gothic cathedral and as the birthplace of physicist Albert Einstein.
- 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_69ca8425f4fc81909c1c586e9a5b7530 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd358846e48190a8aacfab19d88ae7 |
completed | April 1, 2026, 3:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bca63c3c819084aec1c0bb962ffe |
completed | April 5, 2026, 1:36 a.m. |
Created at: March 30, 2026, 7:37 p.m.