Triple
T19783476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Lewis College |
E475196
|
entity |
| Predicate | hasSpecialPolicy |
P31242
|
FINISHED |
| Object | Native American tuition waiver |
—
|
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: Native American tuition waiver | Statement: [Fort Lewis College, hasSpecialPolicy, Native American tuition waiver]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpecialPolicy Context triple: [Fort Lewis College, hasSpecialPolicy, Native American tuition waiver]
-
A.
hasSpecialRules
Indicates that certain entities are governed by additional or exceptional rules that differ from the standard ones.
-
B.
hasSpecial
Indicates that an entity possesses or is associated with a distinctive or exceptional attribute, status, or feature compared to others.
-
C.
hasNotablePolicy
chosen
Indicates that an entity possesses a policy that is distinguished, significant, or otherwise noteworthy in its context.
-
D.
hasSpecialQuota
Indicates that a specific entity is assigned a distinct or exceptional quota differing from the standard allocation.
-
E.
hasPolicyStatus
Indicates that an entity is associated with a policy and specifies the current status or state of that policy.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65385ee8081908d58cc3ff05b9b23 |
completed | April 20, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e53053ed2881908400becdfada7fd3 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:49 p.m.