Triple
T6359449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jay Bouwmeester |
E143072
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jay Bouwmeester |
E143072
|
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: Jay Bouwmeester | Statement: [Jay Bouwmeester, name, Jay Bouwmeester]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jay Bouwmeester Context triple: [Jay Bouwmeester, name, Jay Bouwmeester]
-
A.
Jay Bouwmeester
chosen
Jay Bouwmeester is a Canadian former professional ice hockey defenceman known for his smooth skating, durability, and long NHL career with teams including the Florida Panthers, Calgary Flames, and St. Louis Blues.
-
B.
Jay Van Andel
Jay Van Andel was an American businessman and philanthropist best known as the co-founder of Amway and a major benefactor in Grand Rapids, Michigan.
-
C.
Leo Beenhakker
Leo Beenhakker is a Dutch football manager renowned for coaching top clubs and national teams, including Real Madrid, Ajax, and the Netherlands.
-
D.
Michael Neeleman
Michael Neeleman is a notable individual recognized as a bearer of the Neeleman surname, likely distinguished in a professional or public context.
-
E.
Sander Dieleman
Sander Dieleman is a machine learning researcher known for his influential work in deep learning for audio and music, including contributions to models such as WaveNet.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067f72f8481908f9df0c0cdf22a52 |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d66a8a08190b52bb8302787dac5 |
completed | March 27, 2026, 7:10 a.m. |
Created at: March 22, 2026, 4:32 p.m.