Triple
T21149259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rum and Coca-Cola |
E521140
|
entity |
| Predicate | popularizedCalypso |
P143066
|
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: [Rum and Coca-Cola, popularizedCalypso, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularizedCalypso Context triple: [Rum and Coca-Cola, popularizedCalypso, true]
-
A.
popularizedBy
Indicates that something became widely known, accepted, or fashionable as a result of the influence or actions of a particular agent.
-
B.
popularizedByLyric
Indicates that something became widely known or gained popularity as a result of being mentioned or featured in a song’s lyrics.
-
C.
popularizedIn
Indicates that something became widely known, accepted, or fashionable within a particular place, time period, or context.
-
D.
popularizedAfter
Indicates that one entity became widely known, accepted, or influential only after another specified entity had already gained popularity.
-
E.
popularizedByWork
Indicates that something became widely known, accepted, or influential as a result of a particular work (such as a book, film, or artwork).
- 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_69e0b50c6a848190a4e525a77a319b8a |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e723ffb578819080a000987459a8a5 |
completed | April 21, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69e5f5f8a5bc819081918c7fa8e4496d |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5f993240c8190847c0b08e65726c8 |
completed | April 20, 2026, 10:01 a.m. |
Created at: April 16, 2026, 2:58 p.m.