Triple
T8487027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MIT.nano |
E200855
|
entity |
| Predicate | hasCleanroomClass |
P83720
|
FINISHED |
| Object | Class 100 |
—
|
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: Class 100 | Statement: [MIT.nano, hasCleanroomClass, Class 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCleanroomClass Context triple: [MIT.nano, hasCleanroomClass, Class 100]
-
A.
hasCoreClass
Indicates that an entity is associated with, or belongs to, a primary or fundamental class within a classification system.
-
B.
hasCommunicationClass
Indicates that one entity is assigned to, or associated with, a particular category or class of communication.
-
C.
hasSecurityClass
Indicates that an entity is assigned to or associated with a particular security classification level.
-
D.
hasServiceClass
Indicates that an entity is associated with, or categorized under, a particular class or type of service.
-
E.
hasDocumentClass
Indicates that an entity is associated with, or categorized under, a specific class or type of document.
- F. None of above. chosen
Provenance (4 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_69ca831d7b148190a6e32c1de43ab13b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53c4d608190a766c0e919a4b96f |
completed | March 31, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69cbd107633c8190a36ba50e07876918 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69d4c7c15c708190997122e3e472734f |
completed | April 7, 2026, 9 a.m. |
Created at: March 30, 2026, 6:13 p.m.