Triple
T10000848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strategic Airlift Capability |
E197320
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | SAC |
E197320
|
NE 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: SAC | Statement: [Strategic Airlift Capability, abbreviation, SAC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAC Context triple: [Strategic Airlift Capability, abbreviation, SAC]
-
A.
SAC
The SAC is Myanmar’s military junta that seized power in the 2021 coup and has since served as the country’s de facto governing authority.
-
B.
SAC
SAC is the National Rail station code for St Albans City railway station in Hertfordshire, England.
-
C.
SAC
The SAC is the abbreviated name commonly used for the State Affairs Commission, the top governing body in North Korea responsible for major state policy and leadership.
-
D.
SAC
chosen
SAC is a NATO-led multinational program that provides participating nations with shared strategic airlift capabilities using a fleet of C-17 Globemaster III aircraft.
-
E.
SAC
SAC (Soft Actor-Critic) is a popular off-policy deep reinforcement learning algorithm that optimizes both expected return and policy entropy to achieve stable and efficient learning in continuous control tasks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc8f50888190b2f1c5240cb58e4f |
completed | April 2, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d25854c524819081315b1a8faf335e |
completed | April 5, 2026, 12:40 p.m. |
Created at: March 30, 2026, 8:51 p.m.