Triple
T9185993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betsy |
E220459
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Betsie |
E439629
|
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: Betsie | Statement: [Betsy, hasVariant, Betsie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betsie Context triple: [Betsy, hasVariant, Betsie]
-
A.
Betsie
chosen
Betsie is the commonly used nickname of Betsie Verwoerd, the wife of former South African Prime Minister Hendrik Verwoerd.
-
B.
Kleine Emme
Kleine Emme is a river in central Switzerland that flows through the canton of Lucerne before joining the Reuss River.
-
C.
Het Lieverdje
Het Lieverdje is a small bronze statue of a street urchin in central Amsterdam, symbolizing the city's playful, rebellious youth and serving as a well-known local meeting point.
-
D.
Bittendes Kind
"Bittendes Kind" is one of the short, characterful piano pieces in Robert Schumann's "Kinderszenen," depicting the tender, pleading mood of a child.
-
E.
Tine Havelaar
Tine Havelaar is a character in Multatuli’s novel "Max Havelaar," depicted as the devoted and morally upright wife of the protagonist.
- 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_69ca83e6d77c81909862b7afef56b1bf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc31a52508190a83ccd76f3aa039b |
completed | April 1, 2026, 7:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c1e163c8190bf9a85de7569c14d |
completed | April 4, 2026, 12:32 a.m. |
Created at: March 30, 2026, 7:24 p.m.