Triple
T6038627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacqueline |
E134483
|
entity |
| Predicate | spellingVariant |
P457
|
FINISHED |
| Object | Jaqueline |
E134483
|
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: Jaqueline | Statement: [Jacqueline, spellingVariant, Jaqueline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaqueline Context triple: [Jacqueline, spellingVariant, Jaqueline]
-
A.
Jacqueline
chosen
Jacqueline is a feminine given name most famously borne by former U.S. First Lady Jacqueline Kennedy Onassis.
-
B.
Jacqueline Lamba
Jacqueline Lamba was a French Surrealist painter closely associated with the Paris avant-garde and the artistic circle around André Breton in the 1930s and 1940s.
-
C.
Julianna
Julianna is a feminine given name most notably borne by American actress Julianna Margulies.
-
D.
Jacqueline Feather
Jacqueline Feather is a screenwriter best known for her work on films such as the 1982 musical comedy "Starstruck."
-
E.
Juanita
Juanita is a residential neighborhood in the city of Kirkland, Washington, known for its parks, waterfront access, and suburban community character.
- 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_69c00875db5c819099dd5bb833ec43c2 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056ccac948190a27547878d4db8e4 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16ea1595c8190926a5ed8d230ab4a |
completed | March 23, 2026, 4:47 p.m. |
Created at: March 22, 2026, 4:08 p.m.