Triple
T38226772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Force Warrant Officer ranks (historical) |
E1012165
|
entity |
| Predicate | primaryExpertise |
P159849
|
FINISHED |
| Object | technical specialties |
—
|
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: technical specialties | Statement: [Air Force Warrant Officer ranks (historical), primaryExpertise, technical specialties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryExpertise Context triple: [Air Force Warrant Officer ranks (historical), primaryExpertise, technical specialties]
-
A.
primaryExperience
Indicates that one entity is the main or most significant experience associated with another entity, as opposed to secondary or supporting experiences.
-
B.
primaryInterest
Indicates that one entity is the main or most significant focus of attention, concern, or engagement for another entity.
-
C.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
D.
primaryContributionOf
Indicates that one entity is the main or most significant contribution made by another entity.
-
E.
الاختصاص الرئيسي
chosen
Indicates that one entity is the primary field of specialization or main area of focus for another entity.
- 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_69f76dd25e0c81909f2abd0803e5e3ee |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:30 p.m.