Triple
T12310058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bennington College |
E293452
|
entity |
| Predicate | FieldWorkTermType |
P5567
|
FINISHED |
| Object | mandatory annual internship period |
—
|
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: mandatory annual internship period | Statement: [Bennington College, FieldWorkTermType, mandatory annual internship period]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FieldWorkTermType Context triple: [Bennington College, FieldWorkTermType, mandatory annual internship period]
-
A.
termType
chosen
Indicates the classification or category of a term within a system, specifying what kind of term it is (e.g., type, role, or function) in relation to others.
-
B.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
-
C.
typeOfJurisdictionalWork
Indicates the specific category or nature of legal or administrative authority exercised within a given jurisdiction.
-
D.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec02c008190a56aae60a3d9eff6 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.