Triple
T29155593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blandford Church |
E739030
|
entity |
| Predicate | windowDesigner |
P163349
|
FINISHED |
| Object | Louis Comfort Tiffany |
—
|
NE NERFINISHED |
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: Louis Comfort Tiffany | Statement: [Blandford Church, windowDesigner, Louis Comfort Tiffany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: windowDesigner Context triple: [Blandford Church, windowDesigner, Louis Comfort Tiffany]
-
A.
windowType
Indicates the specific kind or category of window associated with an entity.
-
B.
windowArea
Indicates the total surface area occupied by a window (or windows) in a given context.
-
C.
windowManagement
Indicates the relationship or action of controlling, arranging, or interacting with on-screen windows within a graphical user interface.
-
D.
propDesigner
chosen
Indicates that an entity serves as the designer or creator of a particular property, item, or artifact.
-
E.
windowDecoration
Indicates that one entity serves as a decorative element or ornamentation for a window of another entity.
- 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_69f07cb528fc8190a556b73990c347c8 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f662a99aa88190919e42fb163c338f |
completed | May 2, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69f65c2376a08190be5215171e908e69 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 11:44 a.m.