Triple
T17328605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giorgio Tuinfort |
E420753
|
entity |
| Predicate | notableWorkWith |
P26239
|
FINISHED |
| Object | Afrojack |
E207666
|
NE 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: Afrojack | Statement: [Giorgio Tuinfort, notableWorkWith, Afrojack]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Afrojack Context triple: [Giorgio Tuinfort, notableWorkWith, Afrojack]
-
A.
Afrojack
chosen
Afrojack is a Dutch DJ and electronic music producer known for his influential role in the EDM scene and numerous international club hits.
-
B.
Alesso
Alesso is a Swedish DJ and electronic dance music producer known for his melodic progressive house tracks and high-profile collaborations.
-
C.
Gui Boratto
Gui Boratto is a Brazilian electronic music producer and DJ known for his melodic techno and house productions and remixes.
-
D.
Felix Jaehn
Felix Jaehn is a German DJ and record producer best known internationally for his tropical house remix of "Cheerleader" and other chart-topping dance tracks.
-
E.
Jake One
Jake One is an American hip-hop record producer known for his soulful, sample-based beats and work with artists across the underground and mainstream rap scenes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d42154819093a240f677a63145 |
completed | April 19, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c4dff088190833122dbe2981045 |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.