Triple
T59979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bluebonnet |
E1188
|
entity |
| Predicate | petalMarkings |
P4278
|
FINISHED |
| Object | white spot on upper petal |
—
|
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 spot on upper petal | Statement: [Bluebonnet, petalMarkings, white spot on upper petal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: petalMarkings Context triple: [Bluebonnet, petalMarkings, white spot on upper petal]
-
A.
petalTexture
Indicates the type or quality of surface texture exhibited by a flower’s petals.
-
B.
flowerStructure
Indicates the structural characteristics or organization of a flower, such as the arrangement and form of its parts.
-
C.
flowerType
Indicates the specific kind or category of flower associated with an entity.
-
D.
nationalFlower
Indicates that a particular flower is officially designated as the national flower of a country or region.
-
E.
leafShape
Indicates the characteristic form or outline of a leaf that an entity possesses or exhibits.
- 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_69a24a552ef88190a0df287d68c65cba |
completed | Feb. 28, 2026, 1:52 a.m. |
| NER | Named-entity recognition | batch_69a250e401288190ba12322c9c5f07c9 |
completed | Feb. 28, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69a24e9f40908190a2f4a2111469b733 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a250e2a80881909e5a653260e6f8e0 |
completed | Feb. 28, 2026, 2:20 a.m. |
Created at: Feb. 28, 2026, 1:55 a.m.