Triple
T15939815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pastor T.L. Barrett and the Youth for Christ Choir |
E386527
|
entity |
| Predicate | associatedCityScene |
P49053
|
FINISHED |
| Object | Chicago gospel scene |
—
|
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: Chicago gospel scene | Statement: [Pastor T.L. Barrett and the Youth for Christ Choir, associatedCityScene, Chicago gospel scene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCityScene Context triple: [Pastor T.L. Barrett and the Youth for Christ Choir, associatedCityScene, Chicago gospel scene]
-
A.
cityScene
chosen
Indicates a scene or setting that takes place within an urban or city environment.
-
B.
cityPanorama
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
-
C.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
D.
cityWide
Indicates that something applies to, affects, or extends across an entire city.
-
E.
cityDepicted
Indicates that a work (such as an image, artwork, or document) visually or representationally depicts a particular city.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.