Triple
T31299665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Million Miles (I Love You) |
E798178
|
entity |
| Predicate | basedOnSceneFrom |
P167035
|
FINISHED |
| Object | Purple Rain (film) |
—
|
NE NERFINISHED |
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: Purple Rain (film) | Statement: [A Million Miles (I Love You), basedOnSceneFrom, Purple Rain (film)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnSceneFrom Context triple: [A Million Miles (I Love You), basedOnSceneFrom, Purple Rain (film)]
-
A.
featuresSceneFrom
Indicates that one entity (such as a work or media item) includes or presents a particular scene taken from another entity.
-
B.
originatedScene
chosen
Indicates that one entity is the original source or starting point scene from which another scene, event, or representation is derived.
-
C.
hasInfluenceFromScene
Indicates that something is affected, shaped, or guided by the characteristics or context of a particular scene.
-
D.
partOfScene
Indicates that one entity functions as a component or element within a larger scene or setting involving another entity.
-
E.
isSettingOfScene
Indicates that a particular location, time, or environment serves as the backdrop or context in which a scene takes place.
- 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_69f224e0bd4c8190aab9b29a73f7aa3c |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: April 29, 2026, 9:14 p.m.