Triple

T5137525
Position Surface form Disambiguated ID Type / Status
Subject Memphis Red Sox E115862 entity
Predicate notablePlayer P304 FINISHED
Object Porter Moss
Porter Moss was a prominent Negro league pitcher best known for his standout performances with the Memphis Red Sox in the 1930s and early 1940s.
 E495497 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: Porter Moss | Statement: [Memphis Red Sox, notablePlayer, Porter Moss]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Porter Moss
Context triple: [Memphis Red Sox, notablePlayer, Porter Moss]
  • A. Ben Porterfield
    Ben Porterfield is a technology entrepreneur best known as a co-founder of the business intelligence and data analytics company Looker.
  • B. Porter Strong
    Porter Strong was an American character actor of the silent film era, best remembered for his supporting roles in D.W. Griffith films such as "Way Down East."
  • C. Alec Mazo
    Alec Mazo is a professional ballroom dancer and choreographer best known for his appearances and championship win on the U.S. television show Dancing with the Stars.
  • D. Paul Millspaugh
    Paul Millspaugh is a film editor known for his work on the romantic comedy "Two Can Play That Game."
  • E. Miles Chapin
    Miles Chapin is an American actor known for his character roles in film and television, particularly in comedies and cult favorites from the 1970s and 1980s.
  • 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: Porter Moss
Triple: [Memphis Red Sox, notablePlayer, Porter Moss]
Generated description
Porter Moss was a prominent Negro league pitcher best known for his standout performances with the Memphis Red Sox in the 1930s and early 1940s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Porter Moss
Target entity description: Porter Moss was a prominent Negro league pitcher best known for his standout performances with the Memphis Red Sox in the 1930s and early 1940s.
  • A. Ben Porterfield
    Ben Porterfield is a technology entrepreneur best known as a co-founder of the business intelligence and data analytics company Looker.
  • B. Porter Strong
    Porter Strong was an American character actor of the silent film era, best remembered for his supporting roles in D.W. Griffith films such as "Way Down East."
  • C. Alec Mazo
    Alec Mazo is a professional ballroom dancer and choreographer best known for his appearances and championship win on the U.S. television show Dancing with the Stars.
  • D. Paul Millspaugh
    Paul Millspaugh is a film editor known for his work on the romantic comedy "Two Can Play That Game."
  • E. Miles Chapin
    Miles Chapin is an American actor known for his character roles in film and television, particularly in comedies and cult favorites from the 1970s and 1980s.
  • 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_69bd44459a988190a772a5c2ec6a1965 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7851ed788190a2480cd9e619930f completed March 20, 2026, 4:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69bec4d342c8819088f67c01d3769a6d completed March 21, 2026, 4:18 p.m.
NEDg Description generation batch_69bec5867eac819089ee2c5eddc32a4b completed March 21, 2026, 4:21 p.m.
NED2 Entity disambiguation (via description) batch_69bec5eb535c819097deeb331f9f0f4d completed March 21, 2026, 4:23 p.m.
Created at: March 20, 2026, 1:43 p.m.