Triple
T22885313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wlamir Marques |
E567587
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Wlamir |
—
|
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: Wlamir | Statement: [Wlamir Marques, givenName, Wlamir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wlamir Context triple: [Wlamir Marques, givenName, Wlamir]
-
A.
Wlamir Marques
chosen
Wlamir Marques is a legendary Brazilian basketball player who starred for the national team during the 1950s and 1960s, helping Brazil win multiple FIBA World Championship titles and Olympic medals.
-
B.
Rafael Joseffy
Rafael Joseffy was a renowned Hungarian-American pianist and influential piano teacher of the late 19th and early 20th centuries.
-
C.
Lasker
Lasker is a surname most famously associated with figures such as Emanuel Lasker, the long-reigning World Chess Champion, and Albert Lasker, a pioneering American advertising executive.
-
D.
Aleksandr Levitsky
Aleksandr Levitsky was a Soviet cinematographer known for his work on early silent films, including collaborations with pioneering directors of the 1920s.
-
E.
Arthur Guez
Arthur Guez is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Double DQN algorithm.
- 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_69e2458a92ec81908fc1cd5f6407d2ab |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17fc0cdb081908107d40069d9735f |
completed | April 29, 2026, 3:49 a.m. |
Created at: April 17, 2026, 3:40 p.m.