Triple
T14306809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke of Weissenfels |
E354718
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Weissenfels |
E252109
|
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: Weissenfels | Statement: [Duke of Weissenfels, locatedIn, Weissenfels]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weissenfels Context triple: [Duke of Weissenfels, locatedIn, Weissenfels]
-
A.
Weißenfels
chosen
Weißenfels is a historic town in the German state of Saxony-Anhalt, known for its baroque architecture and former prominence as a ducal residence and industrial center.
-
B.
Wernigerode
Wernigerode is a picturesque German town in Saxony-Anhalt known for its colorful half-timbered houses, medieval castle, and location on the northern slopes of the Harz Mountains.
-
C.
Rudolstadt
Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
-
D.
Haldensleben
Haldensleben is a town in the German state of Saxony-Anhalt, known as an administrative and economic center with historical roots dating back to the Middle Ages.
-
E.
Bischofswerda
Bischofswerda is a small town in the Saxony region of eastern Germany, known as a local commercial and transport hub near the city of Dresden.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b156b0819083f2bd319deed1b6 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff99742618819083bba63ce9f27895 |
completed | May 9, 2026, 8:30 p.m. |
Created at: April 10, 2026, 1:12 a.m.