Triple
T523559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RZA |
E10868
|
entity |
| Predicate | producedFor |
P1576
|
FINISHED |
| Object | Masta Killa |
E65378
|
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: Masta Killa | Statement: [RZA, producedFor, Masta Killa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masta Killa Context triple: [RZA, producedFor, Masta Killa]
-
A.
Masta Killa
chosen
Masta Killa is an American rapper best known as a member of the influential hip hop group Wu-Tang Clan.
-
B.
Scarface
Scarface is the notorious nickname of American gangster Al Capone, one of the most infamous crime bosses of the Prohibition era.
-
C.
Masku
Masku is a municipality in Southwest Finland known for its historical estates and proximity to the city of Turku.
-
D.
White Chicks
White Chicks is a 2004 American comedy film in which two Black FBI agents go undercover as white socialite sisters, known for its over-the-top humor and cultural impact.
-
E.
King Baby
King Baby is a stand-up comedy special by American comedian Jim Gaffigan, known for its observational humor about everyday life, food, and parenting.
- 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1b4f01881908b408357ff113308 |
completed | Feb. 28, 2026, 1:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4b23f97448190bd85a8c85d338039 |
completed | March 1, 2026, 9:40 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.