Triple

T10235588
Position Surface form Disambiguated ID Type / Status
Subject Nan White E243453 entity
Predicate adjacentTo P224 FINISHED
Object Mike Green
Mike Green is a relatively obscure individual known primarily through a spatial or contextual association with Nan White.
E851539 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 Green | Statement: [Nan White, adjacentTo, Mike Green]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mike Green
Context triple: [Nan White, adjacentTo, Mike Green]
  • A. Mike Preston
    Mike Preston is a character known for appearing alongside Jacob King, likely within a shared narrative such as a film, television series, or other fictional work.
  • B. Scott Green
    Scott Green is a former National Football League official best known for serving as a referee in multiple Super Bowls.
  • C. Scott Green
    Scott Green is an American higher-education administrator and business executive who serves as president of the University of Idaho.
  • D. Mike Malloy
    Mike Malloy is a progressive American radio talk show host known for his outspoken, left-leaning political commentary and work on various liberal talk radio networks.
  • E. Mike Gaffey
    Mike Gaffey is a songwriter best known for co-writing the track "RITMO (Bad Boys for Life)."
  • 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 Green
Triple: [Nan White, adjacentTo, Mike Green]
Generated description
Mike Green is a relatively obscure individual known primarily through a spatial or contextual association with Nan White.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mike Green
Target entity description: Mike Green is a relatively obscure individual known primarily through a spatial or contextual association with Nan White.
  • A. Mike Preston
    Mike Preston is a character known for appearing alongside Jacob King, likely within a shared narrative such as a film, television series, or other fictional work.
  • B. Scott Green
    Scott Green is a former National Football League official best known for serving as a referee in multiple Super Bowls.
  • C. Scott Green
    Scott Green is an American higher-education administrator and business executive who serves as president of the University of Idaho.
  • D. Mike Malloy
    Mike Malloy is a progressive American radio talk show host known for his outspoken, left-leaning political commentary and work on various liberal talk radio networks.
  • E. Mike Gaffey
    Mike Gaffey is a songwriter best known for co-writing the track "RITMO (Bad Boys for Life)."
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d20de15c8190a81f3e9803fdfcd1 completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f757b514819087f5d5f659c50c66 completed April 9, 2026, 12:48 a.m.
NEDg Description generation batch_69d6fa2ea97081908395048218c0592b completed April 9, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_69d6fcb5dc4c8190944a423a9d16a4b8 completed April 9, 2026, 1:11 a.m.
Created at: April 6, 2026, 11:22 a.m.