Triple
T15739909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nandina |
E381573
|
entity |
| Predicate | foliageSeasonalChange |
P10587
|
FINISHED |
| Object | leaves often turn red or purple in cold weather |
—
|
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: leaves often turn red or purple in cold weather | Statement: [Nandina, foliageSeasonalChange, leaves often turn red or purple in cold weather]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: foliageSeasonalChange Context triple: [Nandina, foliageSeasonalChange, leaves often turn red or purple in cold weather]
-
A.
foliageSeasonalColor
chosen
Indicates the characteristic color that a plant’s foliage takes on during a particular season.
-
B.
leafColor
Indicates the color or coloration characteristics of a leaf in relation to a plant or plant part.
-
C.
floweringSeason
Indicates the time period during which a plant typically produces flowers.
-
D.
hasSeasonalNature
Indicates that something exhibits characteristics, behavior, or occurrence patterns that vary according to specific seasons or times of the year.
-
E.
leafPhenology
Indicates the timing and pattern of leaf developmental stages (such as budburst, expansion, coloration, and fall) in relation to environmental or seasonal conditions.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:46 a.m.