Triple
T6453271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dodge City Law |
E139925
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object |
Law Dog
Law Dog is the canine-themed mascot representing the Dodge City Law indoor football team.
|
E594560
|
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: Law Dog | Statement: [Dodge City Law, mascot, Law Dog]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Law Dog Context triple: [Dodge City Law, mascot, Law Dog]
-
A.
Hound Law
Hound Law is a hill located within the Tweedsmuir Hills range in the Southern Uplands of Scotland.
-
B.
Above the Law
Above the Law is a 1988 American action film, directed by Andrew Davis and starring Steven Seagal in his film debut, known for its blend of martial arts, crime, and political intrigue.
-
C.
Rogue Lawyer
Rogue Lawyer is a legal thriller novel by John Grisham that follows an unconventional street lawyer who takes on dangerous and morally complex cases.
-
D.
Lawn Dogs
Lawn Dogs is a 1997 independent drama film that explores the unlikely friendship between a young girl from a wealthy family and a working-class lawn caretaker in a restrictive suburban community.
-
E.
The Dog Pound
The Dog Pound is the passionate student cheering section known for creating an energetic home-ice atmosphere at Boston University Terriers men's hockey games.
- 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: Law Dog Triple: [Dodge City Law, mascot, Law Dog]
Generated description
Law Dog is the canine-themed mascot representing the Dodge City Law indoor football team.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Law Dog Target entity description: Law Dog is the canine-themed mascot representing the Dodge City Law indoor football team.
-
A.
Hound Law
Hound Law is a hill located within the Tweedsmuir Hills range in the Southern Uplands of Scotland.
-
B.
Above the Law
Above the Law is a 1988 American action film, directed by Andrew Davis and starring Steven Seagal in his film debut, known for its blend of martial arts, crime, and political intrigue.
-
C.
Rogue Lawyer
Rogue Lawyer is a legal thriller novel by John Grisham that follows an unconventional street lawyer who takes on dangerous and morally complex cases.
-
D.
Lawn Dogs
Lawn Dogs is a 1997 independent drama film that explores the unlikely friendship between a young girl from a wealthy family and a working-class lawn caretaker in a restrictive suburban community.
-
E.
The Dog Pound
The Dog Pound is the passionate student cheering section known for creating an energetic home-ice atmosphere at Boston University Terriers men's hockey games.
- 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_69c008b301948190a35854e5284dc822 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069d1c7c481909df9d2369edf5e74 |
completed | March 22, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bd982208190bbf5f00a85f7098d |
completed | March 27, 2026, 9:20 a.m. |
| NEDg | Description generation | batch_69c64d8fe71881908417dc1d3f242bd5 |
completed | March 27, 2026, 9:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c64e5cd1b88190abcdc8af02991d1d |
completed | March 27, 2026, 9:31 a.m. |
Created at: March 22, 2026, 4:47 p.m.