Triple

T11266324
Position Surface form Disambiguated ID Type / Status
Subject Ashford Stud E266694 entity
Predicate hasStoodStallion P55481 FINISHED
Object Tiz the Law
Tiz the Law is an American Thoroughbred racehorse best known for winning the 2020 Belmont Stakes and Travers Stakes.
 E915026 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: Tiz the Law | Statement: [Ashford Stud, hasStoodStallion, Tiz the Law]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tiz the Law
Context triple: [Ashford Stud, hasStoodStallion, Tiz the Law]
  • A. Mr. Tiger
    Mr. Tiger is the nickname of Al Kaline, the Hall of Fame right fielder who became a legendary figure for the Detroit Tigers in Major League Baseball.
  • B. Tiger Tim
    Tiger Tim is the nickname of Tim Henman, a former British professional tennis player known for his success at Wimbledon and role in revitalizing British tennis in the 1990s and early 2000s.
  • C. Old Tige
    Old Tige was the nickname of William L. Cabell, a Confederate general who later became a prominent postwar civic leader and mayor of Dallas, Texas.
  • D. Lucky the Lion
    Lucky the Lion is the costumed lion mascot representing Texas A&M University–Commerce at athletic events and school functions.
  • E. Tigery
    Tigery is a small commune in the Essonne department of the Île-de-France region in northern France.
  • 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: Tiz the Law
Triple: [Ashford Stud, hasStoodStallion, Tiz the Law]
Generated description
Tiz the Law is an American Thoroughbred racehorse best known for winning the 2020 Belmont Stakes and Travers Stakes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tiz the Law
Target entity description: Tiz the Law is an American Thoroughbred racehorse best known for winning the 2020 Belmont Stakes and Travers Stakes.
  • A. Mr. Tiger
    Mr. Tiger is the nickname of Al Kaline, the Hall of Fame right fielder who became a legendary figure for the Detroit Tigers in Major League Baseball.
  • B. Tiger Tim
    Tiger Tim is the nickname of Tim Henman, a former British professional tennis player known for his success at Wimbledon and role in revitalizing British tennis in the 1990s and early 2000s.
  • C. Old Tige
    Old Tige was the nickname of William L. Cabell, a Confederate general who later became a prominent postwar civic leader and mayor of Dallas, Texas.
  • D. Lucky the Lion
    Lucky the Lion is the costumed lion mascot representing Texas A&M University–Commerce at athletic events and school functions.
  • E. Tigery
    Tigery is a small commune in the Essonne department of the Île-de-France region in northern France.
  • 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_69d6aac8c2f48190ad0596f1f89f0470 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e94e5e3c8190a31995d55d20d7ed completed April 9, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4ccc7fdc48190a84b8b584f67b464 completed April 19, 2026, 12:38 p.m.
NEDg Description generation batch_69e4d9ed6a048190ae7476d44cee6a6e completed April 19, 2026, 1:34 p.m.
NED2 Entity disambiguation (via description) batch_69e4ddb1b4c8819087699bc73610c7f8 completed April 19, 2026, 1:50 p.m.
Created at: April 8, 2026, 9:31 p.m.