Triple
T12438809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donna Jordan |
E297216
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Michael A. Jordan |
E998762
|
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: Michael A. Jordan | Statement: [Donna Jordan, spouse, Michael A. Jordan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael A. Jordan Context triple: [Donna Jordan, spouse, Michael A. Jordan]
-
A.
Michael A. Jordan
chosen
Michael A. Jordan is the father of American actor and producer Michael B. Jordan.
-
B.
Michael H. Jordan
Michael H. Jordan was an American business executive best known for leading major corporations such as Westinghouse Electric and Electronic Data Systems through significant restructuring and turnaround efforts.
-
C.
Michael Bakari Jordan
Michael Bakari Jordan is an American actor and producer best known for his roles in films such as "Fruitvale Station," "Creed," and "Black Panther."
-
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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d8dc0f881908a3da736d8947ce1 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c66449081909484b945b6b97643 |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 8, 2026, 9:55 p.m.