Triple
T17958988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Belt (Auroville) |
E449025
|
entity |
| Predicate | hasLandCoverType |
P2022
|
FINISHED |
| Object | forested area |
—
|
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: forested area | Statement: [Green Belt (Auroville), hasLandCoverType, forested area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandCoverType Context triple: [Green Belt (Auroville), hasLandCoverType, forested area]
-
A.
hasLandCoverage
chosen
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
B.
hasLandUseCharacter
Indicates that one entity possesses or is associated with a particular type or pattern of land use.
-
C.
hasHistoricalLandCover
Indicates that an entity is associated with information about the land cover that existed in a specified area during a past time period.
-
D.
hasLandComponent
Indicates that something includes, consists of, or is associated with a land-based part or portion as one of its components.
-
E.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
- 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b130b7a081908542a3bc6dab5842 |
completed | April 19, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69e3f8f2bd088190b1e22ad4d9cc8b13 |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:22 a.m.