Triple
T10701408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Basketball League |
E252284
|
entity |
| Predicate | averagePlayerSalaryComparedToWNBA |
P95353
|
FINISHED |
| Object | higher |
—
|
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: higher | Statement: [American Basketball League, averagePlayerSalaryComparedToWNBA, higher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averagePlayerSalaryComparedToWNBA Context triple: [American Basketball League, averagePlayerSalaryComparedToWNBA, higher]
-
A.
averagePlayerSalary
Indicates the typical or mean salary earned by players within a specified group or context.
-
B.
numberOfWNBAAllStarSelections
Indicates the count of times an individual has been selected to participate in the WNBA All-Star Game.
-
C.
WNBAAllWNBAFirstTeamSelection
Indicates that an individual has been chosen for the WNBA All-WNBA First Team in a given season, recognizing them as one of the league’s top players at their position.
-
D.
WNBAReboundingLeader
Indicates that the subject is the player who led the WNBA in rebounds for a given season or time period.
-
E.
WNBAFranchiseDebutYear
Indicates the year in which a WNBA franchise first began play in the league.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd8c835c8190bf1a67ee94195926 |
completed | April 9, 2026, 1:14 a.m. |
| PD | Predicate disambiguation | batch_69d6dd8cc0788190b4c02a772e4b58b3 |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df47899481909ac0e518d94883cb |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:12 p.m.