Triple
T1817290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pump It |
E40462
|
entity |
| Predicate | chartPositionSwitzerland |
P32623
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Pump It, chartPositionSwitzerland, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chartPositionSwitzerland Context triple: [Pump It, chartPositionSwitzerland, 2]
-
A.
chartPosition
Indicates the ranking or placement of an item within an ordered chart or list at a specific point in time.
-
B.
chartPositionAustralia
Indicates the position or ranking of something on a music or sales chart specifically within Australia.
-
C.
chartPositionCanada
Indicates the position or ranking of something on a chart specifically within Canada.
-
D.
chartPositionUSCountry
Indicates the position or ranking of something on a music or performance chart specifically within the United States.
-
E.
capitalCityOfLargestCanton
Indicates the city that serves as the capital of the geographically largest canton within a given country or region.
- 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_69a8864526c081908a3a4d74f689e2c5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d884548190a19cf3a6b5ae9d48 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aba67554788190b429f2b9f0a70310 |
completed | March 7, 2026, 4:15 a.m. |
Created at: March 4, 2026, 7:32 p.m.