Triple
T32112807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Route Burn |
E820160
|
entity |
| Predicate | hasSurroundingLandcover |
P2022
|
FINISHED |
| Object | native forest |
—
|
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: native forest | Statement: [Route Burn, hasSurroundingLandcover, native forest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurroundingLandcover Context triple: [Route Burn, hasSurroundingLandcover, native forest]
-
A.
surroundedByLandUse
Indicates that an area or feature is encircled or bordered on all sides by specified types of land use.
-
B.
hasNearbyLandscapeType
Indicates that one entity is located close to, or in the vicinity of, a particular type of landscape.
-
C.
hasLandCoverage
chosen
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
D.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
E.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
- 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_69f3490209c881908ec0241476715f15 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69feecf1bb248190ba30f0bb1d22ee08 |
completed | May 9, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69feea5f27748190b223ee4e3ba5a678 |
completed | May 9, 2026, 8:03 a.m. |
Created at: May 1, 2026, 12:27 a.m.