Triple
T24431360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quarians |
E616007
|
entity |
| Predicate | maskPolicy |
P73638
|
FINISHED |
| Object | rarely remove helmets in non-sterile environments |
—
|
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: rarely remove helmets in non-sterile environments | Statement: [Quarians, maskPolicy, rarely remove helmets in non-sterile environments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maskPolicy Context triple: [Quarians, maskPolicy, rarely remove helmets in non-sterile environments]
-
A.
protectionPolicy
chosen
Indicates that one entity establishes or enforces rules or measures to safeguard another entity from harm, loss, or risk.
-
B.
recognitionPolicy
Indicates the rules or criteria governing how something is identified, acknowledged, or accepted as valid within a given system or context.
-
C.
mask
Indicates that one entity covers, conceals, or obscures another entity, typically to hide its identity, appearance, or specific features.
-
D.
themePolicy
Indicates that a policy, rule, or guideline is the primary subject or focus of an action, event, or statement.
-
E.
eraPolicy
Indicates that a specific policy was in effect during a particular historical or temporal era.
- 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_69e2d7eadb248190a867130fe45f0388 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f29782a4a0819087cb7ba399371520 |
completed | April 29, 2026, 11:42 p.m. |
| PD | Predicate disambiguation | batch_69f287d3237c819099559c00f83131d8 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:16 a.m.