Triple

T17320884
Position Surface form Disambiguated ID Type / Status
Subject Leo Durocher E420555 entity
Predicate nickname P55 FINISHED
Object Leo the Lip
Leo the Lip was the fiery, outspoken Major League Baseball manager and former shortstop Leo Durocher, famed for his combative style and sharp tongue.
E1263584 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: Leo the Lip | Statement: [Leo Durocher, nickname, Leo the Lip]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leo the Lip
Context triple: [Leo Durocher, nickname, Leo the Lip]
  • A. The Lip
    The Lip is a nickname famously associated with legendary American boxer Muhammad Ali, highlighting his quick wit and outspoken personality.
  • B. Lippy the Lion
    Lippy the Lion is a talkative, optimistic cartoon lion created by Hanna-Barbera who starred in early 1960s animated television shorts alongside his hyena sidekick Hardy Har Har.
  • C. Winnie the Bish
    Winnie the Bish is the playful nickname of Winston Bishop, a quirky and lovable character from the TV sitcom "New Girl."
  • D. Pippy
    Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
  • E. Lucky the Lion
    Lucky the Lion is the costumed lion mascot representing Texas A&M University–Commerce at athletic events and school functions.
  • 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: Leo the Lip
Triple: [Leo Durocher, nickname, Leo the Lip]
Generated description
Leo the Lip was the fiery, outspoken Major League Baseball manager and former shortstop Leo Durocher, famed for his combative style and sharp tongue.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Leo the Lip
Target entity description: Leo the Lip was the fiery, outspoken Major League Baseball manager and former shortstop Leo Durocher, famed for his combative style and sharp tongue.
  • A. The Lip
    The Lip is a nickname famously associated with legendary American boxer Muhammad Ali, highlighting his quick wit and outspoken personality.
  • B. Lippy the Lion
    Lippy the Lion is a talkative, optimistic cartoon lion created by Hanna-Barbera who starred in early 1960s animated television shorts alongside his hyena sidekick Hardy Har Har.
  • C. Winnie the Bish
    Winnie the Bish is the playful nickname of Winston Bishop, a quirky and lovable character from the TV sitcom "New Girl."
  • D. Pippy
    Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
  • E. Lucky the Lion
    Lucky the Lion is the costumed lion mascot representing Texas A&M University–Commerce at athletic events and school functions.
  • 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_69d889d22b848190a4663d0b8f8f76e7 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e439cf5394819089bff5f8dc2e8241 completed April 19, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a018c483e988190a481b4c487f79329 completed May 11, 2026, 7:59 a.m.
NEDg Description generation batch_6a018d67ddd081909d227ae3405415ee completed May 11, 2026, 8:03 a.m.
NED2 Entity disambiguation (via description) batch_6a0192dfff1881909aa87f5d23cda870 completed May 11, 2026, 8:27 a.m.
Created at: April 10, 2026, 5:43 a.m.