Triple
T21618284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaToya London |
E533503
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | LaToya |
—
|
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: LaToya | Statement: [LaToya London, givenName, LaToya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LaToya Context triple: [LaToya London, givenName, LaToya]
-
A.
LaToya
chosen
LaToya is a feminine given name most notably associated with American singer and actress LaToya London.
-
B.
Dameisha
Dameisha is a popular coastal area in Shenzhen, China, best known for its long sandy beach, seaside resorts, and recreational attractions.
-
C.
LaTanya
LaTanya is a feminine given name most notably borne by American actress and producer LaTanya Richardson Jackson.
-
D.
Toya Johnson
Toya Johnson is an American reality television personality, author, and entrepreneur best known for her appearances on BET’s "Tiny and Toya" and "Toya: A Family Affair" and for her high-profile relationships in the hip-hop community.
-
E.
LaTisha
LaTisha is a fictional female protagonist, likely a young woman or girl, who serves as the central focus of the story.
- 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3bac4a5c8190919c625c14a54c16 |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 16, 2026, 6:34 p.m.