Triple
T13618844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Springs College |
E325394
|
entity |
| Predicate | laborHours |
P32671
|
FINISHED |
| Object | students typically work at least 20 hours per week |
—
|
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: students typically work at least 20 hours per week | Statement: [Deep Springs College, laborHours, students typically work at least 20 hours per week]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laborHours Context triple: [Deep Springs College, laborHours, students typically work at least 20 hours per week]
-
A.
orderedLabor
Indicates that one entity has requested, commissioned, or directed another entity to perform specific labor or work.
-
B.
labourOf
Indicates that one entity is the work, effort, or labor performed or contributed by another entity.
-
C.
laborNumber
Indicates a relationship where a specific labor or work assignment is identified or referenced by a unique number.
-
D.
workLength
chosen
Indicates the duration or length of time associated with a particular work or task.
-
E.
laborSet
Indicates that a particular labor, task, or work assignment has been designated or scheduled for an entity or context.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae1b3ee481909bd43ded6227a3e5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:50 p.m.