Triple
T5730104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juanita Jones Abernathy |
E126360
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Juanita |
E214153
|
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: Juanita | Statement: [Juanita Jones Abernathy, givenName, Juanita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juanita Context triple: [Juanita Jones Abernathy, givenName, Juanita]
-
A.
Juanita
chosen
Juanita is a feminine given name of Spanish origin commonly used in English- and Spanish-speaking countries.
-
B.
Juanita
Juanita is a residential neighborhood in the city of Kirkland, Washington, known for its parks, waterfront access, and suburban community character.
-
C.
Jacqueline
Jacqueline is a feminine given name most famously borne by former U.S. First Lady Jacqueline Kennedy Onassis.
-
D.
Janet
Janet is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
-
E.
Juanita Vanoy
Juanita Vanoy is a former model and Chicago-based real estate professional best known as the ex-wife of basketball legend Michael Jordan.
- 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_69c0082f723881908ce8bb13a0c0f8b7 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a8cca748190b471c842fd2ce218 |
completed | March 22, 2026, 9:09 p.m. |
Created at: March 22, 2026, 3:47 p.m.