Triple
T8540264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sonny Vaccaro |
E202176
|
entity |
| Predicate | workedWith |
P398
|
FINISHED |
| Object | Nike co-founder Phil Knight |
E46705
|
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: Nike co-founder Phil Knight | Statement: [Sonny Vaccaro, workedWith, Nike co-founder Phil Knight]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nike co-founder Phil Knight Context triple: [Sonny Vaccaro, workedWith, Nike co-founder Phil Knight]
-
A.
Phil Knight
chosen
Phil Knight is an American businessman and philanthropist best known as the co-founder and longtime leader of Nike, Inc.
-
B.
Kevin Plank
Kevin Plank is an American entrepreneur best known as the founder and longtime CEO of the sportswear company Under Armour.
-
C.
Bill Bowerman
Bill Bowerman was an American track and field coach and innovative footwear designer who co-founded Nike and helped revolutionize modern athletic shoe design.
-
D.
Blake Mycoskie
Blake Mycoskie is an American entrepreneur and philanthropist best known as the founder of TOMS Shoes and the pioneer of the “One for One” social enterprise model.
-
E.
Larry Miller
Larry Miller is an American character actor and comedian known for his supporting roles in numerous film comedies and television shows.
- 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_69ca832461e88190a654c5e44e233aa8 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6dfb2bc8190a41e32eca3c824c2 |
completed | March 31, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6d9d06e48190a5c0cfa9779fc07c |
completed | April 2, 2026, 1:22 p.m. |
Created at: March 30, 2026, 6:18 p.m.