Triple
T38461648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tableland area of Mount Katahdin |
E912466
|
entity |
| Predicate | humanImpactConcern |
P162346
|
FINISHED |
| Object | trail erosion |
—
|
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: trail erosion | Statement: [Tableland area of Mount Katahdin, humanImpactConcern, trail erosion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: humanImpactConcern Context triple: [Tableland area of Mount Katahdin, humanImpactConcern, trail erosion]
-
A.
humanImpact
Indicates the effect or influence that human activities have on another entity, system, or environment.
-
B.
humanImpactLevel
Indicates the degree or extent to which human activities affect or influence a given entity, system, or environment.
-
C.
managesHumanImpact
Indicates that an entity oversees, regulates, or mitigates the effects of human activities on another entity or system.
-
D.
impactOnHumans
Indicates a relationship where something produces an effect, influence, or consequence on humans.
-
E.
examinesHumanImpactOnNature
chosen
Indicates examining how human activities affect or alter natural environments and ecosystems.
- 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_69f76e861d8c81908559031dc66e3c15 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcce7afcd08190906cc3801152656a |
completed | May 7, 2026, 5:40 p.m. |
| PD | Predicate disambiguation | batch_69fcccf140ec8190862d53388a5f40d7 |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:31 p.m.