Triple
T29870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | giant sequoia |
E596
|
entity |
| Predicate | reproductiveStructure |
P1979
|
FINISHED |
| Object | woody cones |
—
|
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: woody cones | Statement: [giant sequoia, reproductiveStructure, woody cones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reproductiveStructure Context triple: [giant sequoia, reproductiveStructure, woody cones]
-
A.
flowerStructure
Indicates the structural characteristics or organization of a flower, such as the arrangement and form of its parts.
-
B.
leafArrangement
Indicates how leaves are positioned or organized on a plant’s stem or branches.
-
C.
pollination
Indicates the transfer of pollen from one flower’s reproductive structures to another’s, enabling fertilization and seed production.
-
D.
sexualDimorphism
Indicates differences in physical characteristics between males and females of a species that are systematically associated with their sex.
-
E.
flowerType
Indicates the specific kind or category of flower associated with an entity.
- F. None of above. chosen
Provenance (4 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a2490019948190a89bb0910c60d462 |
completed | Feb. 28, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69a2486d40348190b2d21fc444f499a6 |
completed | Feb. 28, 2026, 1:44 a.m. |
| PDg | Predicate description generation | batch_69a248fef2b881908180bd4e32e58cb5 |
completed | Feb. 28, 2026, 1:46 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.