Triple
T33640054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Control System |
E861806
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Skhye Hutch
Skhye Hutch is a hip-hop music producer known for his work with the Detroit group Control System and other underground rap artists.
|
E2062232
|
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: Skhye Hutch | Statement: [Control System, producer, Skhye Hutch]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Skhye Hutch Triple: [Control System, producer, Skhye Hutch]
Generated description
Skhye Hutch is a hip-hop music producer known for his work with the Detroit group Control System and other underground rap artists.
Provenance (5 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_69f3498280c48190bcc3494017d14234 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f976b3a8819085a42a11674639ce |
completed | May 3, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3627184738819084171a3bc1a569c5 |
completed | June 20, 2026, 5:37 a.m. |
| NEDg | Description generation | batch_6a36289df2dc8190a6265c22c12f5fef |
completed | June 20, 2026, 5:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a362919e1b081909e0d3907323d4e9d |
completed | June 20, 2026, 5:46 a.m. |
Created at: May 1, 2026, 1:42 a.m.