Triple
T19977104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Levels |
E493719
|
entity |
| Predicate | chartPosition |
P15268
|
FINISHED |
| Object | German Singles Chart top 10 |
—
|
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: German Singles Chart top 10 | Statement: [Levels, chartPosition, German Singles Chart top 10]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: German Singles Chart top 10 Context triple: [Levels, chartPosition, German Singles Chart top 10]
-
A.
German Singles Chart
chosen
The German Singles Chart is the official weekly ranking of the most popular singles in Germany, based on sales and streaming data.
-
B.
European Hot 100
The European Hot 100 was a pan-European music singles chart that ranked the most popular songs across numerous European countries.
-
C.
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.
-
D.
Swedish Singles Chart
The Swedish Singles Chart is Sweden's official ranking of the most popular singles, typically based on sales and streaming data.
-
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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d1054e08190993b92b86ec5bbc8 |
completed | April 20, 2026, 5:06 p.m. |
Created at: April 11, 2026, 3:27 p.m.