Triple
T35361465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brickleberry National Park |
E1021495
|
entity |
| Predicate | hasRangerStation |
P105426
|
FINISHED |
| Object | Brickleberry Ranger Station |
—
|
NE NERFINISHED |
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: Brickleberry Ranger Station | Statement: [Brickleberry National Park, hasRangerStation, Brickleberry Ranger Station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRangerStation Context triple: [Brickleberry National Park, hasRangerStation, Brickleberry Ranger Station]
-
A.
hasRangerDistrict
Indicates that an entity is associated with, managed by, or located within a specific ranger district.
-
B.
hasStationAt
chosen
Indicates that an entity maintains or operates a station located at a specified place.
-
C.
hasFireStation
Indicates that a location or area contains or is served by a fire station.
-
D.
hasGagingStation
Indicates that one entity is equipped with, or associated with, a gaging station used to measure and monitor conditions such as water level or flow.
-
E.
hasStationNear
Indicates that one entity has a station located in close proximity to another entity.
- 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_69f76def44c881908a20e8008572eb44 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd5f29b1988190877764ef2a399c7f |
completed | May 8, 2026, 3:57 a.m. |
| PD | Predicate disambiguation | batch_69fd5e30194c819085b5ce586122ab37 |
completed | May 8, 2026, 3:53 a.m. |
Created at: May 3, 2026, 4:03 p.m.