Triple

T1501628
Position Surface form Disambiguated ID Type / Status
Subject ESPN Radio E33807 entity
Predicate subjectOf P38 FINISHED
Object Mike and Mike
Mike and Mike was a popular ESPN Radio morning sports talk show co-hosted by Mike Greenberg and Mike Golic that blended sports analysis with humor and pop culture.
 E171151 NE FINISHED

How this triple was built (4 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: Mike and Mike | Statement: [ESPN Radio, subjectOf, Mike and Mike]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mike and Mike
Context triple: [ESPN Radio, subjectOf, Mike and Mike]
  • A. MIKE
    MIKE is a high-resolution optical spectrograph used on the Magellan Telescopes for detailed astronomical spectroscopy.
  • B. Pat and Mike
    Pat and Mike is a 1952 sports comedy film starring Katharine Hepburn and Spencer Tracy, known for its witty script and depiction of a female athlete challenging gender norms.
  • C. Micheal
    Micheal is a given name, typically a variant spelling of the more common name Michael.
  • D. Mick
    Mick is the commonly used nickname of American politician and former White House Chief of Staff Mick Mulvaney.
  • E. Michaël
    Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mike and Mike
Triple: [ESPN Radio, subjectOf, Mike and Mike]
Generated description
Mike and Mike was a popular ESPN Radio morning sports talk show co-hosted by Mike Greenberg and Mike Golic that blended sports analysis with humor and pop culture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mike and Mike
Target entity description: Mike and Mike was a popular ESPN Radio morning sports talk show co-hosted by Mike Greenberg and Mike Golic that blended sports analysis with humor and pop culture.
  • A. MIKE
    MIKE is a high-resolution optical spectrograph used on the Magellan Telescopes for detailed astronomical spectroscopy.
  • B. Pat and Mike
    Pat and Mike is a 1952 sports comedy film starring Katharine Hepburn and Spencer Tracy, known for its witty script and depiction of a female athlete challenging gender norms.
  • C. Micheal
    Micheal is a given name, typically a variant spelling of the more common name Michael.
  • D. Mick
    Mick is the commonly used nickname of American politician and former White House Chief of Staff Mick Mulvaney.
  • E. Michaël
    Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
  • F. None of above. chosen

Provenance (5 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_69a885f352a4819099b24ff15489dede completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a8872e41848190b35b37f32aef784f completed March 4, 2026, 7:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad1cb3c5908190b3d5fe7a4dcaa234 completed March 8, 2026, 6:52 a.m.
NEDg Description generation batch_69ad202409bc81908733c966b6a64a37 completed March 8, 2026, 7:07 a.m.
NED2 Entity disambiguation (via description) batch_69ad207aa360819089bd06f9aa0ee86f completed March 8, 2026, 7:08 a.m.
Created at: March 4, 2026, 7:24 p.m.