Triple
T11307328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Pill Blues |
E267749
|
entity |
| Predicate | hasElectronicInfluences |
P98420
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Red Pill Blues, hasElectronicInfluences, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasElectronicInfluences Context triple: [Red Pill Blues, hasElectronicInfluences, true]
-
A.
influencedTechnology
Indicates that one entity has had a causal or shaping impact on the development, design, or use of a technological entity.
-
B.
hasGenreInfluenceOn
Indicates that one genre has a notable impact on shaping or influencing the characteristics, style, or development of another genre.
-
C.
hasRetroInfluence
Indicates that something is influenced by or incorporates stylistic or conceptual elements from an earlier time or past era.
-
D.
hasUrbanInfluence
Indicates that one entity exerts or reflects the characteristics, impact, or style of an urban area on another entity.
-
E.
hasPopularityInfluencedBy
Indicates that the popularity level of one entity is affected or shaped by another specified factor or entity.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9bf87d88190904c2d174578ebbf |
completed | April 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69d787aa31888190860eecaa80da5b20 |
completed | April 9, 2026, 11:04 a.m. |
| PDg | Predicate description generation | batch_69d796d049e88190a9fd7508f477f541 |
completed | April 9, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:32 p.m.