Triple
T391176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Botanical Garden |
E8883
|
entity |
| Predicate | numberOfPlantSpecies |
P6211
|
FINISHED |
| Object | more than 20,000 |
—
|
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: more than 20,000 | Statement: [Berlin Botanical Garden, numberOfPlantSpecies, more than 20,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPlantSpecies Context triple: [Berlin Botanical Garden, numberOfPlantSpecies, more than 20,000]
-
A.
numberOfSpecies
chosen
Indicates the count of distinct species associated with a given entity or context.
-
B.
notableTreeSpecies
Indicates that the subject place or area is known for, or characterized by, the specified tree species.
-
C.
plantType
Indicates the specific kind or category of plant that an entity is classified as.
-
D.
involvesPlant
Indicates that the relationship or action includes or pertains to a plant as a participating entity.
-
E.
numberOfRoseVarieties
Indicates the quantitative count of distinct rose varieties associated with an entity.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5d73e881909101308a583c8f13 |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e96960608190bdd342da9c5ddb5e |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.