Triple
T18387100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Missouri River (Arkansas) |
E446618
|
entity |
| Predicate | hasAdjacentLandCover |
P85343
|
FINISHED |
| Object | mixed hardwood 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: mixed hardwood forest | Statement: [Little Missouri River (Arkansas), hasAdjacentLandCover, mixed hardwood forest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdjacentLandCover Context triple: [Little Missouri River (Arkansas), hasAdjacentLandCover, mixed hardwood forest]
-
A.
hasNearbyLandscapeType
chosen
Indicates that one entity is located close to, or in the vicinity of, a particular type of landscape.
-
B.
hasLandCoverage
Indicates that a specified area or region is covered or occupied by a particular type of land surface or 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.
isAdjacentTo
Indicates that one entity is directly next to or bordering another without anything of the same type in between.
-
E.
hasLandUseCharacter
Indicates that one entity possesses or is associated with a particular type or pattern 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e517a114c08190af1be1ae52c83b63 |
completed | April 19, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:46 a.m.