Triple
T423381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jo Jo White |
E8152
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Jo Jo |
E8152
|
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: Jo Jo | Statement: [Jo Jo White, nickname, Jo Jo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jo Jo Context triple: [Jo Jo White, nickname, Jo Jo]
-
A.
Jo Jo White
chosen
Jo Jo White was an American Hall of Fame point guard best known for leading the Boston Celtics to two NBA championships in the 1970s and earning NBA Finals MVP in 1976.
-
B.
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
C.
Jussy
Jussy is a rural municipality in western Switzerland known for its vineyards and countryside within the canton of Geneva.
-
D.
Joe Zee
Joe Zee is a Canadian-born fashion stylist, editor, and television personality known for his influential work in fashion media and style-focused TV shows.
-
E.
Jack
Jack is a common masculine given name, often used as a familiar form of John and widely featured in English-language literature and popular culture.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eec200648190bcb9f1b98c8e9cdf |
completed | Feb. 28, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4429ec70c8190aaff2e0e6af82612 |
completed | March 1, 2026, 1:43 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.