Triple
T4806259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Air Force Security Service network |
E106953
|
entity |
| Predicate | staffSpecialty |
P39000
|
FINISHED |
| Object | cryptologic analysts |
—
|
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: cryptologic analysts | Statement: [United States Air Force Security Service network, staffSpecialty, cryptologic analysts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: staffSpecialty Context triple: [United States Air Force Security Service network, staffSpecialty, cryptologic analysts]
-
A.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
C.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
D.
styleSpecialty
Indicates a relationship where an entity’s expertise, focus, or specialization is in a particular style or stylistic approach.
-
E.
teamSpecialty
chosen
Indicates the particular area of expertise or focus that characterizes a team’s skills or activities.
- 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_69bd43f6a1e08190bf0a372bfc336ee5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ff981fc819080d4466c6fe06cf3 |
completed | March 20, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1c43a48190a65e56b1624a2339 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:23 p.m.