Triple
T1202018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meuse |
E25802
|
entity |
| Predicate | tributary |
P415
|
FINISHED |
| Object | Moselle |
E66813
|
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: Moselle | Statement: [Meuse, tributary, Moselle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moselle Context triple: [Meuse, tributary, Moselle]
-
A.
Moselle
Moselle is a department in northeastern France, bordering Germany and Luxembourg, known for its strategic location, industrial history, and mixed French-German cultural heritage.
-
B.
Moselle River
chosen
The Moselle River is a major European waterway flowing through France, Luxembourg, and Germany, renowned for its scenic valleys and wine-producing regions.
-
C.
Rhens
Rhens is a historic town on the Rhine River in western Germany, known for its medieval role as a meeting place of the prince-electors of the Holy Roman Empire.
-
D.
Meuse
The Meuse is a major European river flowing through France, Belgium, and the Netherlands, historically important for transport, trade, and the development of surrounding regions.
-
E.
Meuse
Meuse is a department in northeastern France known for its rural landscapes and significant World War I battlefields, including Verdun.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd9fece4819089a6a2d61e61fa2e |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada957e50c8190a7ee94d4ce220ab8 |
completed | March 8, 2026, 4:52 p.m. |
Created at: March 1, 2026, 7:46 p.m.