Triple
T17389431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Suze |
E422776
|
entity |
| Predicate | hasNameInFrench |
P6538
|
FINISHED |
| Object | Suze |
—
|
NE NERFINISHED |
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: Suze | Statement: [River Suze, hasNameInFrench, Suze]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suze Context triple: [River Suze, hasNameInFrench, Suze]
-
A.
Suze
chosen
The Suze is a river in western Switzerland that flows through the Jura region and the city of Biel/Bienne before emptying into Lake Biel.
-
B.
Suze
Suze is the nickname of Suze Rotolo, an American artist and political activist best known for her relationship with Bob Dylan in the early 1960s.
-
C.
Suzie
Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
-
D.
Suzy
Suzy is a fictional character from the film "Cashback," portrayed by actress Michelle Ryan.
-
E.
Suzy
Suzy is the central female protagonist of John Steinbeck’s novel "Sweet Thursday," known for her independent spirit and evolving relationship with Doc in the Cannery Row community.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d710288190bf0f4762801fefae |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a8c718c81909cb20749aaf12897 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:45 a.m.