Triple
T9425345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Wanna Be Your Lover |
E227250
|
entity |
| Predicate | crossoverSuccess |
P56239
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [I Wanna Be Your Lover, crossoverSuccess, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossoverSuccess Context triple: [I Wanna Be Your Lover, crossoverSuccess, yes]
-
A.
hasCrossovers
Indicates that one entity features or participates in crossover appearances or interactions with another entity or set of entities.
-
B.
crossesOverWith
Indicates that one entity intersects or overlaps with another, typically by passing across or through its path, boundary, or extent.
-
C.
crossoverHit
chosen
Indicates that a creative work has achieved significant popularity or success across multiple distinct audience segments, genres, or markets beyond its original target.
-
D.
hasCrossoverHits
Indicates that an entity (such as an artist or work) has achieved significant success across multiple distinct genres, markets, or audience categories.
-
E.
featuresCrossoverWith
Indicates that one entity includes or participates in a crossover event or collaboration with another entity.
- 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_69ca8436ba308190903e470776d2d893 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7c8f59dc8190854dfc0d287731c6 |
completed | April 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69cca550777c819094e1851a6127cbbc |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:49 p.m.