Triple
T8088681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Place du Carrousel |
E188799
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Seine |
E6962
|
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: Seine | Statement: [Place du Carrousel, near, Seine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seine Context triple: [Place du Carrousel, near, Seine]
-
A.
Rhine
The Rhine is one of Europe's most important rivers, historically serving as a vital trade route and cultural boundary from the Alps through Germany to the North Sea.
-
B.
River Seine
chosen
The River Seine is a major waterway in northern France that flows through the heart of Paris and is central to the city's history, culture, and landscape.
-
C.
Eure River
The Eure River is a tributary of the Seine in northern France that flows through the city of Chartres and several other towns in the Normandy and Centre-Val de Loire regions.
-
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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb421c717c819089dd88c30a6401aa |
completed | March 31, 2026, 3:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbe81763881909e2e67bb56f2aa83 |
completed | April 1, 2026, 6:43 a.m. |
Created at: March 30, 2026, 5:29 p.m.