Triple
T7014934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alipurduar |
E162676
|
entity |
| Predicate | hasWildlifeTourism |
P55845
|
FINISHED |
| Object | elephant safaris in nearby parks |
—
|
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: elephant safaris in nearby parks | Statement: [Alipurduar, hasWildlifeTourism, elephant safaris in nearby parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWildlifeTourism Context triple: [Alipurduar, hasWildlifeTourism, elephant safaris in nearby parks]
-
A.
hasWildlifeIssue
Indicates that an entity is affected by, involved in, or responsible for a problem or conflict related to wildlife.
-
B.
hasTourismResource
chosen
Indicates that a place, area, or entity possesses or is associated with a tourism-related resource, attraction, or facility.
-
C.
hasTourismFunction
Indicates that an entity serves a role or purpose related to tourism, such as attracting, accommodating, or providing services to tourists.
-
D.
hasNearbyWildlife
Indicates that there is wildlife located close to or in the immediate vicinity of the referenced entity.
-
E.
containsWildernessArea
Indicates that one entity geographically includes or encompasses a designated wilderness area within its boundaries.
- 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_69c6885a127c8190867b059bdccf13ff |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e5ecd4488190bf19e42de55da98b |
completed | March 27, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69c6e1b8118481909d76eb6616160e80 |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:34 p.m.