Triple
T944574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Longhorns |
E20383
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object |
Bevo
Bevo is the live longhorn steer that serves as the iconic symbol of the University of Texas at Austin’s athletic teams.
|
E111063
|
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: Bevo | Statement: [Texas Longhorns, mascot, Bevo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bevo Context triple: [Texas Longhorns, mascot, Bevo]
-
A.
Barkley
Barkley is a surname most notably associated with Alben W. Barkley, the 35th vice president of the United States under President Harry S. Truman.
-
B.
Buck
Buck is a surname most prominently associated with American sportscaster Joe Buck, known for his play-by-play commentary on major baseball and football broadcasts.
-
C.
Bronk
Bronk is a surname most notably associated with Detlev W. Bronk, an influential American scientist and educator who helped shape modern biophysics and higher education policy.
-
D.
Baylor Bear
Baylor Bear is the costumed bear mascot representing Baylor University's athletic teams and school spirit.
-
E.
Buffalo Bull
Buffalo Bull is the official mascot character of the Japanese professional baseball team Orix Buffaloes.
- 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: Bevo Triple: [Texas Longhorns, mascot, Bevo]
Generated description
Bevo is the live longhorn steer that serves as the iconic symbol of the University of Texas at Austin’s athletic teams.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bevo Target entity description: Bevo is the live longhorn steer that serves as the iconic symbol of the University of Texas at Austin’s athletic teams.
-
A.
Barkley
Barkley is a surname most notably associated with Alben W. Barkley, the 35th vice president of the United States under President Harry S. Truman.
-
B.
Buck
Buck is a surname most prominently associated with American sportscaster Joe Buck, known for his play-by-play commentary on major baseball and football broadcasts.
-
C.
Bronk
Bronk is a surname most notably associated with Detlev W. Bronk, an influential American scientist and educator who helped shape modern biophysics and higher education policy.
-
D.
Baylor Bear
Baylor Bear is the costumed bear mascot representing Baylor University's athletic teams and school spirit.
-
E.
Buffalo Bull
Buffalo Bull is the official mascot character of the Japanese professional baseball team Orix Buffaloes.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3a3ed3881908386af140477c514 |
completed | March 1, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a826e585208190bf477bf78d162e84 |
completed | March 4, 2026, 12:34 p.m. |
| NEDg | Description generation | batch_69a83365d590819085d8e92c1a69aa10 |
completed | March 4, 2026, 1:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a834268c388190ac725f48be8f8ea6 |
completed | March 4, 2026, 1:31 p.m. |
Created at: March 1, 2026, 7:40 p.m.