Triple
T22389571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | If I Let You Go |
E553478
|
entity |
| Predicate | chartedIn |
P19348
|
FINISHED |
| Object | Swedish Singles Chart |
—
|
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: Swedish Singles Chart | Statement: [If I Let You Go, chartedIn, Swedish Singles Chart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Swedish Singles Chart Context triple: [If I Let You Go, chartedIn, Swedish Singles Chart]
-
A.
Swedish Singles Chart
chosen
The Swedish Singles Chart is Sweden's official ranking of the most popular singles, typically based on sales and streaming data.
-
B.
Norwegian Singles Chart
The Norwegian Singles Chart is Norway’s official ranking of the most popular singles, typically based on sales and streaming data.
-
C.
German Singles Chart
The German Singles Chart is the official weekly ranking of the most popular singles in Germany, based on sales and streaming data.
-
D.
Dutch Singles Chart
The Dutch Singles Chart is a national music ranking in the Netherlands that lists the most popular singles based on sales, streaming, and airplay.
-
E.
Swiss Singles Chart
The Swiss Singles Chart is Switzerland’s official ranking of the most popular singles, typically based on sales and streaming data.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4cf87c8190a1ff474daec326b7 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15858c13c819098fe66a50ecea7d7 |
completed | April 29, 2026, 1:01 a.m. |
Created at: April 16, 2026, 8:45 p.m.