Triple
T5996411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steven Sinofsky |
E133480
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Steven Sinofsky |
E133480
|
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: Steven Sinofsky | Statement: [Steven Sinofsky, name, Steven Sinofsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steven Sinofsky Context triple: [Steven Sinofsky, name, Steven Sinofsky]
-
A.
Steven Sinofsky
chosen
Steven Sinofsky is an American technology executive and former Microsoft president best known for leading the development of Windows and Office.
-
B.
Eddy Cue
Eddy Cue is a senior Apple executive best known for overseeing the company’s internet software and services, including iTunes, the App Store, and iCloud.
-
C.
Steve Ballmer
Steve Ballmer is an American businessman and former Microsoft CEO known for his energetic leadership style and ownership of the NBA’s Los Angeles Clippers.
-
D.
Scott Belsky
Scott Belsky is an American entrepreneur, author, and investor best known as the co-founder of the creative platform Behance and as a longtime product leader at Adobe.
-
E.
Phil Schiller
Phil Schiller is a longtime Apple executive who has played a key role in the company’s product marketing and major keynote presentations.
- 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_69c00870ddbc81909880fa3864f4f38d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04e963f3c819082dd755e328ab947 |
completed | March 22, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c10876d4d0819083ac7431c8abaedd |
completed | March 23, 2026, 9:31 a.m. |
Created at: March 22, 2026, 4:05 p.m.