Triple
T36485067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cupola module |
E898913
|
entity |
| Predicate | windowProtection |
P185581
|
FINISHED |
| Object | external shutters |
—
|
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: external shutters | Statement: [Cupola module, windowProtection, external shutters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: windowProtection Context triple: [Cupola module, windowProtection, external shutters]
-
A.
windowManagement
Indicates the relationship or action of controlling, arranging, or interacting with on-screen windows within a graphical user interface.
-
B.
windowPolicy
Indicates how windows are allowed, restricted, or managed within a given context or system.
-
C.
windowType
Indicates the specific kind or category of window associated with an entity.
-
D.
coreProtection
Indicates that an entity provides essential safeguarding or defense for another entity’s central or most critical component.
-
E.
windowArea
Indicates the total surface area occupied by a window (or windows) in a given context.
- 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_69f76e5a0e088190a2b6706aeb41723c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bccf05bc8190b61fdb2b2a315811 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be9b9ab481908328e0e8d8ac73d4 |
completed | May 3, 2026, 9:31 p.m. |
Created at: May 3, 2026, 4:10 p.m.