Triple
T4497094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pegnitz River |
E100723
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Pegnitz (town) |
E98867
|
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: Pegnitz (town) | Statement: [Pegnitz River, flowsThrough, Pegnitz (town)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pegnitz (town) Context triple: [Pegnitz River, flowsThrough, Pegnitz (town)]
-
A.
Pegnitz
chosen
Pegnitz is a river in the German state of Bavaria that flows through cities such as Nuremberg and Bayreuth before joining the Rednitz to form the Regnitz.
-
B.
Altenburg
Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
-
C.
Lützen
Lützen is a town in present-day Germany best known as the site of the 1632 Battle of Lützen during the Thirty Years' War, where Swedish King Gustavus Adolphus was killed.
-
D.
Bitterfeld-Wolfen
Bitterfeld-Wolfen is a town in Saxony-Anhalt, Germany, known for its industrial heritage, particularly in chemical production and film manufacturing.
-
E.
Weiterstadt
Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
- 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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56bf3ff48190b3aae0136d7fce45 |
completed | March 20, 2026, 2:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd67c4e7c88190b9b9cab49444b515 |
completed | March 20, 2026, 3:29 p.m. |
Created at: March 20, 2026, 1 p.m.