Triple
T18358478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy Wood |
E439854
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Flowers in the Rain |
—
|
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 in the Rain | Statement: [Roy Wood, notableWork, Flowers in the Rain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flowers in the Rain Context triple: [Roy Wood, notableWork, Flowers in the Rain]
-
A.
Flowers in the Rain
chosen
"Flowers in the Rain" is a 1967 psychedelic pop single by the British rock band The Move, known for being the first song ever played on BBC Radio 1.
-
B.
Flowers Never Bend with the Rainfall
"Flowers Never Bend with the Rainfall" is a folk song by Paul Simon, known from Simon & Garfunkel’s 1966 album *Parsley, Sage, Rosemary and Thyme*.
-
C.
Love in the Rain
Love in the Rain is an Egyptian film featuring renowned actress Faten Hamama in a leading role.
-
D.
Flower Fields
Flower Fields is a vibrant, flower-filled area in the Paper Mario series known for its colorful scenery and plant-themed characters and puzzles.
-
E.
Land of Flowers
Land of Flowers is a fantastical, flower-themed realm featured as one of the magical kingdoms in Disney’s film "The Nutcracker and the Four Realms."
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e516d88bc481909bcfd2e8984b7216 |
completed | April 19, 2026, 5:54 p.m. |
Created at: April 10, 2026, 10:37 a.m.