Triple

T2087973
Position Surface form Disambiguated ID Type / Status
Subject Katrina Maley Wheeler E32597 entity
Predicate spouse P13 FINISHED
Object Ted Wheeler E1861 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: Ted Wheeler | Statement: [Katrina Maley Wheeler, spouse, Ted Wheeler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ted Wheeler
Context triple: [Katrina Maley Wheeler, spouse, Ted Wheeler]
  • A. Ted Wheeler chosen
    Ted Wheeler is an American politician and member of the Democratic Party who has served as the mayor of Portland, Oregon, overseeing the city through periods of significant protest and policy debate.
  • B. Brad Cox
    Brad Cox was an American computer scientist and software engineer best known for co-creating the Objective-C programming language.
  • C. Ryan Wilkinson
    Ryan Wilkinson is a relatively obscure individual whose primary public mention appears to be as a namesake in reference data rather than as a widely recognized public figure.
  • D. Sam Wheeler
    Sam Wheeler is the father of Ted Wheeler, the mayor of Portland, Oregon.
  • E. Tod Nielsen
    Tod Nielsen is a technology executive known for senior leadership roles at major software companies, including serving as a top executive at Borland and later at firms like Salesforce and Heroku.
  • 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_69a885eba0708190999696a45cbec816 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abba712388819091d68a4bb99f6b17 completed March 7, 2026, 5:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2742834c8190ad9be71128959e0c completed March 9, 2026, 1:49 a.m.
Created at: March 4, 2026, 7:43 p.m.