Triple
T346956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Seine |
E6962
|
entity |
| Predicate | tributary |
P415
|
FINISHED |
| Object | Aube |
E43606
|
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: Aube | Statement: [River Seine, tributary, Aube]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aube Context triple: [River Seine, tributary, Aube]
-
A.
Aube
chosen
Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
-
B.
Val-d'Oise
Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
-
C.
Oise
Oise is a major river in northern France that flows through regions such as Picardy and Île-de-France before joining the Seine near Paris.
-
D.
Creuse
Creuse is a rural department in central France known for its sparsely populated landscapes, traditional agriculture, and part of the historic Limousin region.
-
E.
Nièvre
Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
- 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb1a37c08190b1380f6bf8513a37 |
completed | Feb. 28, 2026, 1:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3fe8f7af481908942ab7872a45ab3 |
completed | March 1, 2026, 8:53 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.