Triple
T12332829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trouble |
E294005
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | T-Mix |
E979713
|
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: T-Mix | Statement: [Trouble, producer, T-Mix]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: T-Mix Context triple: [Trouble, producer, T-Mix]
-
A.
T-Mix
chosen
T-Mix is a music producer best known for his work on the debut album "Soul Survivor."
-
B.
The Mix
The Mix is a UK-based digital support service offering free, confidential help and counseling for young people facing mental health, relationship, and life challenges.
-
C.
MIX
MIX is Donald Knuth’s hypothetical computer architecture used in *The Art of Computer Programming* to illustrate and analyze algorithms in a machine-level context.
-
D.
Le Grand Mix
Le Grand Mix is a renowned live music venue and cultural hub in Tourcoing, France, known for hosting diverse contemporary music performances and events.
-
E.
Mixed Me!
"Mixed Me!" is a children's picture book by Taye Diggs that celebrates a biracial child's identity, confidence, and sense of self.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f64ad20819080d99e57833b4b51 |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6346a3f34819091882004ab558347 |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:53 p.m.