Triple
T30083350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Third Psalm Tune by Thomas Tallis |
E764532
|
entity |
| Predicate | textureInOriginalUse |
P168812
|
FINISHED |
| Object | homophonic psalm tune |
—
|
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: homophonic psalm tune | Statement: [Third Psalm Tune by Thomas Tallis, textureInOriginalUse, homophonic psalm tune]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textureInOriginalUse Context triple: [Third Psalm Tune by Thomas Tallis, textureInOriginalUse, homophonic psalm tune]
-
A.
usesTexture
Indicates that one entity employs or incorporates the texture of another entity in its appearance, design, or representation.
-
B.
primaryTexture
Indicates that one entity serves as the main or dominant surface texture characterizing another entity.
-
C.
texture
Indicates the surface quality or feel of an entity as perceived by touch or appearance, such as being smooth, rough, soft, or coarse.
-
D.
textureTreatment
Indicates how an entity’s surface feel or texture has been modified, processed, or treated.
-
E.
imageMaterial
Indicates that one entity serves as the material or physical medium from which the image entity is composed or rendered.
- 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67d6a9ae08190ba91cbe92980e3f0 |
completed | May 2, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
| PDg | Predicate description generation | batch_69f676f73c3481909f01fa69851b7298 |
completed | May 2, 2026, 10:13 p.m. |
Created at: April 29, 2026, 7:03 p.m.