Triple
T28114879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Stephen’s Church with Chagall windows |
E710591
|
entity |
| Predicate | hasWindowDesigner |
P169953
|
FINISHED |
| Object | Marc Chagall |
—
|
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: Marc Chagall | Statement: [St. Stephen’s Church with Chagall windows, hasWindowDesigner, Marc Chagall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWindowDesigner Context triple: [St. Stephen’s Church with Chagall windows, hasWindowDesigner, Marc Chagall]
-
A.
hasDomeDesigner
Indicates that an entity’s dome was designed or created by a specific designer or architect.
-
B.
hasWindowStyle
Indicates that an entity possesses or is characterized by a particular style or type of window.
-
C.
hasWindowDepicting
Indicates that one entity possesses a window on which the other entity is depicted or represented.
-
D.
officeHeldByDesigner
Indicates that a particular office or official position is occupied or held by a given designer.
-
E.
hasWindowsShape
Indicates that an object possesses a particular geometric or stylistic form characteristic of windows.
- 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_69ef9b72f63081909dfbc2c1ddae86c6 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f688d015908190ad5df37030ecf332 |
completed | May 2, 2026, 11:29 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f688034580819086a0f9100645f8ba |
completed | May 2, 2026, 11:25 p.m. |
Created at: April 27, 2026, 9:13 p.m.