Triple
T6223279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William G. Morgan |
E139167
|
entity |
| Predicate | sportCreatedAsAlternativeTo |
P68999
|
FINISHED |
| Object | basketball |
—
|
LITERAL FINISHED |
How this triple was built (2 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: basketball | Statement: [William G. Morgan, sportCreatedAsAlternativeTo, basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportCreatedAsAlternativeTo Context triple: [William G. Morgan, sportCreatedAsAlternativeTo, basketball]
-
A.
sportsAndRecreation
Indicates a relationship where an entity is associated with, involved in, or designated for sports or recreational activities.
-
B.
sportCategory
Indicates that one entity is classified as a type or category of sport to which the other entity (typically a specific sport or sporting event) belongs.
-
C.
popularSport
Indicates that a sport is widely liked, followed, or played by many people within a certain group or region.
-
D.
sportsName
Indicates the specific sport associated with or played in a given context or event.
-
E.
sportsCategory
Indicates that one entity is classified as a type or category within the domain of sports to which the other entity belongs.
- F. None of above. chosen
Provenance (4 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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062c06c7881909999ba44aa4a23f9 |
completed | March 22, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69c055ffdf54819086d987d646e44ff5 |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:22 p.m.