Triple
T13589207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French preparatory classes |
E324646
|
entity |
| Predicate | tuitionLevel |
P110184
|
FINISHED |
| Object | low tuition compared to private institutions |
—
|
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: low tuition compared to private institutions | Statement: [French preparatory classes, tuitionLevel, low tuition compared to private institutions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tuitionLevel Context triple: [French preparatory classes, tuitionLevel, low tuition compared to private institutions]
-
A.
tuitionFee
Indicates the amount of money that must be paid for instruction or enrollment in an educational program.
-
B.
tuitionFeeType
Indicates the category or structure of tuition fees that applies to an entity (such as full-time, part-time, in-state, out-of-state, or other fee types).
-
C.
tuitionBased
Indicates that something operates, is determined, or is structured based on the amount or presence of tuition fees.
-
D.
isTuitionFreeFor
Indicates that one entity does not require another entity to pay tuition fees for access, enrollment, or participation.
-
E.
admissionsLevel
Indicates the degree or category of access, entry, or acceptance granted in an admissions context.
- F. None of above. chosen
Provenance (4 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb055cc98819091fab597b69e5e3e |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
| PDg | Predicate description generation | batch_69dbaf9f3bdc8190838539aaef1f422b |
completed | April 12, 2026, 2:43 p.m. |
Created at: April 9, 2026, 9:49 p.m.