Triple
T20886247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richmond Flowers Sr. |
E514287
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Flowers |
—
|
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: Flowers | Statement: [Richmond Flowers Sr., familyName, Flowers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flowers Context triple: [Richmond Flowers Sr., familyName, Flowers]
-
A.
Flowers
chosen
Flowers is a common English surname shared by various notable individuals across fields such as sports, music, and politics.
-
B.
Flowers
"Flowers" is a 2023 pop single by Miley Cyrus that became a global hit for its empowering lyrics about self-love and independence.
-
C.
Flowers
"Flowers" is a poignant song from the musical *Hadestown* that explores themes of love, loss, and memory through Eurydice’s perspective.
-
D.
Flowers
Flowers is a dark British comedy-drama television series that explores the dysfunctional lives of the eccentric Flowers family.
-
E.
Flowers and Trees
Flowers and Trees is a 1932 Disney animated short film celebrated as the first commercially released cartoon produced in full three-strip Technicolor.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6d058d4dc81908398f8c75e30dc77 |
completed | April 21, 2026, 1:18 a.m. |
Created at: April 16, 2026, 12:46 p.m.