Triple
T4386451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belmont Stakes |
E99253
|
entity |
| Predicate | traditionalFlower |
P579
|
FINISHED |
| Object | White carnations |
—
|
LITERAL FINISHED |
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: White carnations | Statement: [Belmont Stakes, traditionalFlower, White carnations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalFlower Context triple: [Belmont Stakes, traditionalFlower, White carnations]
-
A.
flowerType
chosen
Indicates the specific kind or category of flower associated with an entity.
-
B.
nationalFlower
Indicates that a particular flower is officially designated as the national flower of a country or region.
-
C.
traditionalMotif
Indicates that something incorporates, represents, or is characterized by a motif rooted in established cultural or historical traditions.
-
D.
floweringUse
Indicates the use or application of something specifically for flowering or promoting the flowering process.
-
E.
flowerSymbolMeaning
Indicates that a particular flower is used to represent or convey a specific symbolic meaning or message.
- F. None of above.
Provenance (3 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352669f608190b3aa7030d8073e04 |
completed | March 12, 2026, 11:55 p.m. |
| PD | Predicate disambiguation | batch_69b34f572efc8190bad1e5078cbcb75a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:19 p.m.