Triple
T99493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Town of Keene, New York |
E2007
|
entity |
| Predicate | hasNaturalResource |
P2856
|
FINISHED |
| Object | forests |
—
|
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: forests | Statement: [Town of Keene, New York, hasNaturalResource, forests]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNaturalResource Context triple: [Town of Keene, New York, hasNaturalResource, forests]
-
A.
naturalResources
chosen
Indicates that one entity possesses, contains, or is a source of natural resources for another entity.
-
B.
hasNaturalFeature
Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
-
C.
hasNature
Indicates that something possesses, exhibits, or is characterized by a particular inherent quality, essence, or fundamental type.
-
D.
hasLandform
Indicates that one entity possesses, contains, or is characterized by a particular natural landform.
-
E.
hasHydrosphere
Indicates that an entity possesses or is characterized by a surrounding layer or system of water, such as oceans, seas, lakes, or other bodies of liquid water.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a253b95d4c81909d1f2bc37e799c44 |
completed | Feb. 28, 2026, 2:32 a.m. |
| PD | Predicate disambiguation | batch_69a24ebfc5a88190bdd1653b9fa541fe |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.