Triple

T10101912
Position Surface form Disambiguated ID Type / Status
Subject Schultz E216222 entity
Predicate hasNotableBearer P458 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: [Schultz, hasNotableBearer, Howard Schultz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Howard Schultz
Context triple: [Schultz, hasNotableBearer, 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. Mike O’Leary
    Mike O’Leary is a British football executive best known for serving as chairman of Ipswich Town Football Club.
  • C. 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.
  • D. Anthony Hsieh
    Anthony Hsieh is an American entrepreneur best known as the founder and former CEO of loanDepot, one of the largest nonbank mortgage lenders in the United States.
  • E. David Cote
    David Cote is an American business executive best known for serving as the longtime CEO of Honeywell International and for his involvement in U.S. fiscal and economic policy discussions.
  • 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_69ca83d039f08190b9d10363221c69fb completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cdd099c21c819097aac4f0f168a2da completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6d3efec8190b1432ca614aeb334 completed April 5, 2026, 7:24 p.m.
Created at: March 30, 2026, 9:02 p.m.