Triple
T75029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Observer Corps |
E1500
|
entity |
| Predicate | workforce |
P1211
|
FINISHED |
| Object | civilian volunteers |
—
|
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: civilian volunteers | Statement: [Observer Corps, workforce, civilian volunteers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workforce Context triple: [Observer Corps, workforce, civilian volunteers]
-
A.
employedPeople
chosen
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
B.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
-
C.
laborSystem
Indicates the type or structure of work organization, employment arrangements, and labor relations that govern how work is performed and managed.
-
D.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
worksWith
Indicates that two entities collaborate or perform tasks together in a shared work-related 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_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.