Triple
T1477219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chhattisgarh |
E30868
|
entity |
| Predicate | forestCoverCharacteristic |
P29206
|
FINISHED |
| Object | high forest cover percentage |
—
|
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: high forest cover percentage | Statement: [Chhattisgarh, forestCoverCharacteristic, high forest cover percentage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: forestCoverCharacteristic Context triple: [Chhattisgarh, forestCoverCharacteristic, high forest cover percentage]
-
A.
forestArea
Indicates the extent or size of land covered by forest within a given area or region.
-
B.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
C.
isUrbanForest
Indicates that an area of trees and vegetation is located within or closely integrated with an urban or suburban environment.
-
D.
landscapeType
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
E.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
- 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c603f9e88190b340734709534860 |
completed | March 1, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69a4c484e52c81908948ff8c0a42751b |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c57984088190b2c2d2d9cc2e5df9 |
completed | March 1, 2026, 11:02 p.m. |
Created at: March 1, 2026, 8:11 p.m.