Triple
T30677785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Swear |
E780966
|
entity |
| Predicate | countryVersionGenre |
P200645
|
FINISHED |
| Object | country |
—
|
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: country | Statement: [I Swear, countryVersionGenre, country]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryVersionGenre Context triple: [I Swear, countryVersionGenre, country]
-
A.
musicGenreRegion
Indicates the association between a music genre and the geographic region where it originates, is most prevalent, or is culturally significant.
-
B.
hasPrimaryGenreRegion
Indicates that an entity’s main or dominant genre is associated with a particular geographic region.
-
C.
countryAudience
Indicates that an audience is located in, associated with, or targeted within a specific country.
-
D.
homeGamesCountry
Indicates the country in which an entity’s home games are played.
-
E.
RBPopVersionGenre
Indicates a relationship where a specific version of a pop song is associated with a particular musical genre.
- 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_69f224a7fc208190a07d6d3879b31640 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff9d9cb4f8819083682be3c483b599 |
completed | May 9, 2026, 8:48 p.m. |
| PD | Predicate disambiguation | batch_69ff9c38bf9c8190bbb85b32f3ae3d2e |
completed | May 9, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69ff9d9ba1ac8190a0cca5764bb5904d |
completed | May 9, 2026, 8:48 p.m. |
Created at: April 29, 2026, 8:32 p.m.