Triple
T22905419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goodbye & Good Riddance |
E568431
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Taz Taylor |
—
|
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: Taz Taylor | Statement: [Goodbye & Good Riddance, producer, Taz Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taz Taylor Context triple: [Goodbye & Good Riddance, producer, Taz Taylor]
-
A.
Taz Taylor
chosen
Taz Taylor is an American record producer and founder of the Internet Money collective, known for crafting melodic trap and hip-hop hits for major artists.
-
B.
Troy Taylor
Troy Taylor is an American R&B record producer and songwriter known for his work with artists such as Trey Songz and Whitney Houston.
-
C.
Tom Tully
Tom Tully was an American character actor known for his prolific work in mid-20th-century film and television, often portraying gruff but sympathetic authority figures.
-
D.
Kent Taylor
Kent Taylor was an American film and television actor best known for his roles in Westerns and crime dramas during the mid-20th century.
-
E.
Dan Taylor
Dan Taylor is a character in the classic British comedy film "The Titfield Thunderbolt," which centers on villagers trying to save their local railway line.
- 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_69e2458cd9e48190943ad2e34485d939 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180198fe88190b2f8c2a827d95fdc |
completed | April 29, 2026, 3:50 a.m. |
Created at: April 17, 2026, 3:41 p.m.