Triple
T75043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Observer Corps |
E1500
|
entity |
| Predicate | typeOfVolunteerService |
P1366
|
FINISHED |
| Object | part-time |
—
|
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: part-time | Statement: [Observer Corps, typeOfVolunteerService, part-time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfVolunteerService Context triple: [Observer Corps, typeOfVolunteerService, part-time]
-
A.
typeOfWork
chosen
Indicates the kind or category of work associated with or performed by an entity.
-
B.
typeOfSupport
Indicates the kind or category of assistance, help, or backing provided in a given context.
-
C.
receivedVolunteersFrom
Indicates that one entity accepted or was provided with volunteers originating from another entity.
-
D.
sponsoringOrganizationType
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
E.
voluntary
Indicates that an action or relationship is undertaken by an entity by free choice, without coercion or obligation.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25314bd6c81908d1cfd4b83f20049 |
completed | Feb. 28, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_69a24eae77ec81909015906f31f2b62e |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.