Triple
T20118975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How High |
E490546
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Stacey Sher |
—
|
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: Stacey Sher | Statement: [How High, producer, Stacey Sher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stacey Sher Context triple: [How High, producer, Stacey Sher]
-
A.
Stacey Sher
chosen
Stacey Sher is an American film and television producer known for her work on acclaimed movies such as "Django Unchained," "Pulp Fiction," and "Erin Brockovich."
-
B.
Stacy Haiduk
Stacy Haiduk is an American actress known for her work in television, including prominent roles in series such as seaQuest DSV, Superboy, and various daytime soap operas.
-
C.
Valerie Faris
Valerie Faris is an American film and music video director best known for co-directing acclaimed features such as "Little Miss Sunshine" and other character-driven comedies and dramas.
-
D.
Jodi Wexler
Jodi Wexler is an actress best known for her role in the film "The Love Machine."
-
E.
Lesli Linka Glatter
Lesli Linka Glatter is an American television director and producer known for her work on acclaimed series such as Homeland, Mad Men, and The West Wing.
- 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.