Triple
T20211954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flowering |
E493508
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Flowering |
—
|
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: Flowering | Statement: [Flowering, title, Flowering]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flowering Context triple: [Flowering, title, Flowering]
-
A.
Flowering
chosen
"Flowering" is an episode of the BBC nature documentary series "The Private Life of Plants," focusing on how plants reproduce through flowers and the intricate strategies they use to attract pollinators.
-
B.
Blooming
Blooming is a professional football club from Bolivia that competes in the country's top-tier league.
-
C.
Flowers
Flowers is a dark British comedy-drama television series that explores the dysfunctional lives of the eccentric Flowers family.
-
D.
Flowers
"Flowers" is a poignant song from the musical *Hadestown* that explores themes of love, loss, and memory through Eurydice’s perspective.
-
E.
Flowers
Flowers is a common English surname shared by various notable individuals across fields such as sports, music, and politics.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ed627f48190a8ba638b85977af3 |
completed | April 20, 2026, 6:22 p.m. |
Created at: April 11, 2026, 11:38 p.m.