Triple
T2553085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howard Schultz |
E56670
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Howard Schultz |
E56670
|
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: Howard Schultz | Statement: [Howard Schultz, name, Howard Schultz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Howard Schultz Context triple: [Howard Schultz, name, Howard Schultz]
-
A.
Howard Schultz
chosen
Howard Schultz is an American businessman best known for transforming Starbucks into a global coffeehouse chain and serving multiple terms as its CEO and chairman.
-
B.
Roger Conant
Roger Conant was an early English colonist and leader best known for establishing the settlement that became Salem, Massachusetts in the 17th century.
-
C.
A. G. Lafley
A. G. Lafley is an American businessman best known for serving as the longtime CEO of Procter & Gamble, where he led major brand expansions and corporate growth.
-
D.
Tony Hsieh
Tony Hsieh was an American entrepreneur and venture capitalist best known for transforming Zappos into a pioneering online retailer celebrated for its customer service–driven culture.
-
E.
Jeff Bewkes
Jeff Bewkes is an American media executive best known for leading Time Warner through major strategic shifts, including the spin-off of its cable division and the expansion of its television and film assets.
- 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_69ab4a4bfec081908039988ec4c86e28 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd30bc6388190b78f2f931eb54041 |
completed | March 7, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d17ecd0819097c6b95307cc7557 |
completed | March 9, 2026, 11:51 p.m. |
Created at: March 6, 2026, 9:48 p.m.