Triple
T7829162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mamba Sports Academy |
E181321
|
entity |
| Predicate | notableVisitor |
P11004
|
FINISHED |
| Object | Kobe Bryant |
E31901
|
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: Kobe Bryant | Statement: [Mamba Sports Academy, notableVisitor, Kobe Bryant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kobe Bryant Context triple: [Mamba Sports Academy, notableVisitor, Kobe Bryant]
-
A.
Kobe Bryant
chosen
Kobe Bryant was an American professional basketball player, primarily with the Los Angeles Lakers, widely regarded as one of the greatest players in NBA history.
-
B.
Bryant
Bryant is a common English surname borne by numerous notable figures in American history, literature, sports, and public life.
-
C.
Bryant
Bryant is the middle name of James B. Conant, the influential American chemist, educator, and president of Harvard University.
-
D.
Michael Jordan
Michael Jordan is a legendary American basketball player widely regarded as one of the greatest athletes in the history of the sport.
-
E.
Michael Jordan
Michael Jordan is a prominent computer scientist and statistician known for his influential work in machine learning, probabilistic graphical models, and statistical inference.
- 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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb04aaed1881908e1da129a43ef9c7 |
completed | March 30, 2026, 11:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdecc6c5c8190af4445928ce1132f |
completed | March 31, 2026, 2:48 p.m. |
Created at: March 30, 2026, 4:44 p.m.