Triple
T21803217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Andean dry puna ecoregion |
E538286
|
entity |
| Predicate | characteristicFlora |
P83264
|
FINISHED |
| Object | bunchgrasses |
—
|
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: bunchgrasses | Statement: [Central Andean dry puna ecoregion, characteristicFlora, bunchgrasses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characteristicFlora Context triple: [Central Andean dry puna ecoregion, characteristicFlora, bunchgrasses]
-
A.
associatedFlora
chosen
Indicates a relationship where specific plants or vegetation are characteristically linked to, occur with, or are commonly found in association with a given entity or environment.
-
B.
studiedFloraOf
Indicates that a subject conducted research or examination on the plant life (flora) of a specified object or region.
-
C.
flowerCharacteristic
Indicates that a flower possesses a particular attribute, quality, or feature (such as color, shape, size, or scent).
-
D.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
E.
typicalLeafCharacteristic
Indicates the usual or defining features of a leaf that characterize it under normal 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_69e0c4733f4081909a86622e7e6d15d2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0780126e88190a93dd8d0519eb8fc |
completed | April 28, 2026, 9:04 a.m. |
| PD | Predicate disambiguation | batch_69e6be751ce881909badced245ef76c7 |
completed | April 21, 2026, 12:01 a.m. |
Created at: April 16, 2026, 6:53 p.m.