Triple
T20146300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gadchiroli district forests |
E491310
|
entity |
| Predicate | hasForestProduce |
P138847
|
FINISHED |
| Object | bamboo |
—
|
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: bamboo | Statement: [Gadchiroli district forests, hasForestProduce, bamboo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasForestProduce Context triple: [Gadchiroli district forests, hasForestProduce, bamboo]
-
A.
hasForestType
Indicates that an area or location is characterized by a specific type or classification of forest.
-
B.
isForested
Indicates that an area or region is covered predominantly by forest or dense tree vegetation.
-
C.
hasForestAssociation
Indicates an ecological or contextual relationship in which an entity is associated with, influenced by, or characteristically occurs in forest environments.
-
D.
hasTrees
Indicates that something possesses or contains one or more trees.
-
E.
hasWood
Indicates that one entity possesses, contains, or is made of wood in relation to another entity or context.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6679e43a48190b3a5da5710b07ff7 |
completed | April 20, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69e54cfb0d0081908e789b9b57e96668 |
completed | April 19, 2026, 9:45 p.m. |
| PDg | Predicate description generation | batch_69e54fc2bc3c819088c33cd263303433 |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 11, 2026, 11:33 p.m.