Triple
T10082771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HK33 |
E213942
|
entity |
| Predicate | safetySelector |
P63490
|
FINISHED |
| Object | ambidextrous on some variants |
—
|
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: ambidextrous on some variants | Statement: [HK33, safetySelector, ambidextrous on some variants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetySelector Context triple: [HK33, safetySelector, ambidextrous on some variants]
-
A.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
B.
hasSafetyCharacteristic
chosen
Indicates that an entity possesses a specific safety-related property, feature, or attribute.
-
C.
safetyProfile
Indicates the overall level and characteristics of risk or harm associated with something, typically summarizing how safe it is under specified conditions.
-
D.
measuresSafetyUsing
Indicates that an entity evaluates or assesses safety by employing a specified method, tool, or standard.
-
E.
safetyRationale
Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd04352d081908f676444cd2d2578 |
completed | April 2, 2026, 2:11 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9 p.m.