Triple
T516571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaveri |
E10721
|
entity |
| Predicate | supportsCrop |
P1897
|
FINISHED |
| Object | paddy |
—
|
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: paddy | Statement: [Kaveri, supportsCrop, paddy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsCrop Context triple: [Kaveri, supportsCrop, paddy]
-
A.
includesCrop
Indicates that one entity (such as a field, farm, or agricultural area) contains or has within it a specified crop.
-
B.
supportsFeature
Indicates that one entity provides, enables, or is compatible with a particular feature or capability of another.
-
C.
supportsField
Indicates that one entity provides the necessary structure, stability, or backing for a particular field, area, or domain associated with another entity.
-
D.
crop
Indicates the action of cutting or trimming part of an object or image, typically to remove unwanted outer areas while keeping a selected region.
-
E.
majorCrop
chosen
Indicates that a particular crop is one of the primary or most important crops cultivated in a given area or context.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f184c3a481909bf60bb627b0ea88 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2f0151e8c81909a82b58ac0515eba |
completed | Feb. 28, 2026, 1:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.