Triple
T35210976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lander, Wyoming |
E1016673
|
entity |
| Predicate | hasOutdoorSchool |
P94463
|
FINISHED |
| Object | NOLS headquarters |
—
|
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: NOLS headquarters | Statement: [Lander, Wyoming, hasOutdoorSchool, NOLS headquarters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOutdoorSchool Context triple: [Lander, Wyoming, hasOutdoorSchool, NOLS headquarters]
-
A.
hasOutdoorEducation
chosen
Indicates that an entity provides, includes, or is associated with educational activities or programs conducted in outdoor or natural environments.
-
B.
hasOutdoorType
Indicates that an entity is associated with a specific type or category of outdoor environment or feature.
-
C.
hasOutdoorResource
Indicates that an entity is associated with, provides, or includes access to an outdoor resource or facility.
-
D.
hasSnowSportsSchool
Indicates that a place or facility offers an organized school or program for learning and practicing snow sports.
-
E.
associatedOutdoorProgram
Indicates that one entity is linked to or participates in a particular outdoor program connected with 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_69f76ddf549c8190869d0af076fd2c28 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78f63c8788190b253a18de5ca1312 |
completed | May 3, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69f78e2d71248190b850c2802ec170c0 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:02 p.m.