Triple
T11295862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riverdale Country School |
E267448
|
entity |
| Predicate | collegePreparatory |
P87610
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Riverdale Country School, collegePreparatory, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: collegePreparatory Context triple: [Riverdale Country School, collegePreparatory, true]
-
A.
collegePreparation
chosen
Indicates the extent to which one entity equips or readies another for success in college-level academic and social demands.
-
B.
college
Indicates that an entity is a college-level educational institution attended by or associated with another entity.
-
C.
collegeHonors
Indicates that an individual has received formal academic honors or distinctions from a college.
-
D.
majorSchool
Indicates that one entity is the primary or most significant school or educational institution associated with another entity.
-
E.
nearCollege
Indicates that one entity is located close to or in the vicinity of a college.
- 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_69d6aac993a08190a6f36445ebaf9a43 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9a262e08190a7c1eacbc6019496 |
completed | April 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69d787a6ca2c8190afdc24b61ccd3f8a |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:32 p.m.