Triple
T10769495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basílica de Santa Maria del Pi |
E254036
|
entity |
| Predicate | hasRoseWindow |
P95871
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Basílica de Santa Maria del Pi, hasRoseWindow, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRoseWindow Context triple: [Basílica de Santa Maria del Pi, hasRoseWindow, yes]
-
A.
supportsWindowSystem
Indicates that one entity provides compatibility with, or operational support for, a particular windowing system used to manage graphical user interfaces.
-
B.
supportsWindowedMode
Indicates that an entity provides or allows operation in a windowed (non-fullscreen) display mode.
-
C.
usesVisualMotif
Indicates that one entity employs a recurring visual element or pattern as a motif in relation to another entity or context.
-
D.
windowManagementStyle
Indicates how windows are organized, displayed, and controlled within a user interface or system.
-
E.
supportsMultipleWindows
Indicates that the subject can handle or display more than one window or view simultaneously.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d732307fb88190ba1447f68523c58a |
completed | April 9, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69d6f311529c819080ca5493d55d6050 |
completed | April 9, 2026, 12:30 a.m. |
| PDg | Predicate description generation | batch_69d6fa323564819097b207eb53f8a9b8 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:16 p.m.