Triple
T22037800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magic |
E544255
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mustard |
—
|
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: Mustard | Statement: [Magic, producer, Mustard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mustard Context triple: [Magic, producer, Mustard]
-
A.
Mustard
chosen
Mustard is an American record producer and DJ known for his minimalist, club-oriented West Coast hip hop sound and numerous chart-topping collaborations.
-
B.
Colman’s Mustard
Colman’s Mustard is a historic British mustard brand, famed for its distinctive hot English mustard powder and long-standing production heritage.
-
C.
Mustardinha
Mustardinha is a neighborhood located in the city of Recife, in northeastern Brazil.
-
D.
Wasabi
Wasabi is a cautious yet skilled member of the superhero team in Disney's animated film "Big Hero 6," known for his precision, laser-based weaponry, and rule-abiding personality.
-
E.
Wasabi
Wasabi is a French action-comedy film known for its blend of fast-paced thrills and humor, directed by Gérard Krawczyk and starring Jean Reno.
- 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f32edc81909b6898af6621f56f |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:25 p.m.