Triple
T20118987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How High |
E490546
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Lark Voorhies |
—
|
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: Lark Voorhies | Statement: [How High, castMember, Lark Voorhies]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lark Voorhies Context triple: [How High, castMember, Lark Voorhies]
-
A.
Lark Voorhies
chosen
Lark Voorhies is an American actress best known for playing Lisa Turtle on the teen sitcom "Saved by the Bell."
-
B.
Christy Carlson Romano
Christy Carlson Romano is an American actress and singer best known for her roles on Disney Channel series such as "Even Stevens" and as the voice of the title character in "Kim Possible."
-
C.
Sarah Hyland
Sarah Hyland is an American actress best known for playing Haley Dunphy on the hit television sitcom "Modern Family."
-
D.
Melissa Joan Hart
Melissa Joan Hart is an American actress best known for her lead roles in the 1990s television series "Clarissa Explains It All" and "Sabrina the Teenage Witch."
-
E.
Kelsey Dohring
Kelsey Dohring is an actress known for playing Chrissy Seaver on the television sitcom "Growing Pains."
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6673c32bc8190a52875961fbcc5e2 |
completed | April 20, 2026, 5:49 p.m. |
Created at: April 11, 2026, 11:30 p.m.