Triple
T8787875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Traunstein district |
E209087
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Siegsdorf |
E776536
|
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: Siegsdorf | Statement: [Traunstein district, contains, Siegsdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegsdorf Context triple: [Traunstein district, contains, Siegsdorf]
-
A.
Siegsdorf
chosen
Siegsdorf is a Bavarian town in southeastern Germany known for its scenic Alpine surroundings and proximity to the Chiemsee lake.
-
B.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
C.
Schopsdorf
Schopsdorf is a small village and former municipality in the Jerichower Land district of Saxony-Anhalt, Germany.
-
D.
Köstendorf
Köstendorf is a small Austrian municipality in the state of Salzburg, known for its rural character and proximity to the city of Salzburg.
-
E.
Lengsdorf
Lengsdorf is a district of the Bonn borough of Hardtberg in Germany, known for its residential character and proximity to both urban amenities and surrounding green areas.
- 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f89a84c819085d4cfe4e6dfbda8 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d02f6e221081909a8a83f2e465b1c2 |
completed | April 3, 2026, 9:21 p.m. |
Created at: March 30, 2026, 6:43 p.m.