Triple
T27916841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colonel, United States Marine Corps Reserve |
E706096
|
entity |
| Predicate | typicalStatus |
P201943
|
FINISHED |
| Object | reserve-duty officer |
—
|
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: reserve-duty officer | Statement: [Colonel, United States Marine Corps Reserve, typicalStatus, reserve-duty officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStatus Context triple: [Colonel, United States Marine Corps Reserve, typicalStatus, reserve-duty officer]
-
A.
typicalVisaStatus
Indicates the usual or most common visa status associated with an entity in a given context.
-
B.
typicalBusinessStatus
Indicates the usual or standard operational status or condition of a business (e.g., active, inactive, seasonal) under normal circumstances.
-
C.
typicalServiceStatus
Indicates the usual or standard operational state or condition that a service is expected to be in under normal circumstances.
-
D.
typicalCustomsStatus
Indicates the usual or standard customs-related condition or classification that typically applies to an entity or transaction.
-
E.
typicalStudentStatus
Indicates the usual or standard enrollment or academic standing that a student typically holds within an educational 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_69ef96b6cc808190aab19fb18b235f4b |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_6a00383e868c819098fd17e25fcbdb04 |
completed | May 10, 2026, 7:48 a.m. |
| PD | Predicate disambiguation | batch_6a0037cc59688190b7b9da939a413db3 |
completed | May 10, 2026, 7:46 a.m. |
| PDg | Predicate description generation | batch_6a00383d83c08190af7bc00f17affd97 |
completed | May 10, 2026, 7:48 a.m. |
Created at: April 27, 2026, 6:54 p.m.