Triple
T20466094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamsweg District |
E502052
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Ramingstein |
—
|
NE NERFINISHED |
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: Ramingstein | Statement: [Tamsweg District, hasSettlement, Ramingstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ramingstein Context triple: [Tamsweg District, hasSettlement, Ramingstein]
-
A.
Ramingstein
chosen
Ramingstein is a small Austrian municipality in the Lungau region of Salzburg, known for its alpine setting and historic silver mining heritage.
-
B.
Marlenheim
Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
-
C.
Benrath
Benrath is a German surname most notably associated with the late actor Martin Benrath.
-
D.
Benrath
Benrath is a district of Düsseldorf in western Germany, known for its historic Benrath Palace and surrounding park.
-
E.
Rheinhausen
Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ae5f1081908768b0c9a3a0bf38 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e696aa0794819082c9989b1f7e9f37 |
completed | April 20, 2026, 9:12 p.m. |
Created at: April 16, 2026, 11:33 a.m.