Triple
T332573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ralph Adams Cram (architectural partnership) |
E6656
|
entity |
| Predicate | designSpecialty |
P466
|
FINISHED |
| Object | religious buildings |
—
|
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: religious buildings | Statement: [Ralph Adams Cram (architectural partnership), designSpecialty, religious buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designSpecialty Context triple: [Ralph Adams Cram (architectural partnership), designSpecialty, religious buildings]
-
A.
designUse
Indicates that one entity is used as a design basis, purpose, or intended functional use for another entity.
-
B.
hasDesign
Indicates that one entity possesses, embodies, or is characterized by a particular design associated with another entity.
-
C.
custom
Indicates that something is specially created, configured, or tailored for a particular purpose, context, or user rather than being standard or generic.
-
D.
designedIn
Indicates that something was created, planned, or conceived during a particular time period or at a specific location.
-
E.
hasSpecialty
chosen
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eab1a7048190ac690ddc2e294914 |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94d99cc8190a112e4b630ec63c1 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.