Triple
T20166992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas G. Stemberg |
E491847
|
entity |
| Predicate | coFounded |
P104
|
FINISHED |
| Object | Staples |
—
|
NE NERFINISHED |
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: Staples | Statement: [Thomas G. Stemberg, coFounded, Staples]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Staples Context triple: [Thomas G. Stemberg, coFounded, Staples]
-
A.
Staples
Staples is a small city in Guadalupe County, Texas, known for its rural character and location along the San Marcos River.
-
B.
Staples
Staples is a small central Minnesota city that serves as a regional hub for transportation, education, and local commerce.
-
C.
Staples
Staples is a surname most prominently associated with American singer and civil rights activist Mavis Staples.
-
D.
Staples Inc.
chosen
Staples Inc. is a major American office supply retail company known for its chain of stores and business-to-business services.
-
E.
Office Depot
Office Depot is a major American office supply retail chain that sells stationery, furniture, technology products, and business services to consumers and companies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66844e49081909b7e9ec2b65cc61d |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:35 p.m.