Triple
T7216032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lookouts |
E149536
|
entity |
| Predicate | sceneLabel |
P75488
|
FINISHED |
| Object | Gilman Street punk 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: Gilman Street punk scene | Statement: [The Lookouts, sceneLabel, Gilman Street punk scene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sceneLabel Context triple: [The Lookouts, sceneLabel, Gilman Street punk scene]
-
A.
sceneFeature
Indicates a characteristic, element, or attribute that is present within or helps define a particular scene.
-
B.
partOfScene
Indicates that one entity functions as a component or element within a larger scene or setting involving another entity.
-
C.
labelLocation
Indicates that a label or identifier is associated with a specific spatial or geographic location.
-
D.
notableScene
Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
-
E.
cityScene
Indicates a scene or setting that takes place within an urban or city environment.
- 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_69c687eca814819095abb52316b1af80 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e99000dc81908ef4b70729cc00b0 |
completed | March 27, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69c6e75f84e481909e7866186ae80cff |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e889854481908c765ce2107f2d3a |
completed | March 27, 2026, 8:28 p.m. |
Created at: March 27, 2026, 2:53 p.m.