Triple
T1208941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Football Bowl Subdivision |
E25953
|
entity |
| Predicate | maximumScholarshipsPerTeam |
P24662
|
FINISHED |
| Object | 85 |
—
|
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: 85 | Statement: [Football Bowl Subdivision, maximumScholarshipsPerTeam, 85]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumScholarshipsPerTeam Context triple: [Football Bowl Subdivision, maximumScholarshipsPerTeam, 85]
-
A.
scholarshipLimitRelativeToFBS
Indicates a relationship where a scholarship limit is defined or constrained in comparison to the scholarship limits used in the Football Bowl Subdivision (FBS).
-
B.
hasNumberOfTeams
Indicates the quantity of teams associated with or contained by a given entity.
-
C.
teamRosterLimit
Indicates the maximum number of members that are allowed to be on a team’s roster.
-
D.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
E.
numberOfTeamsVariesBetween
Indicates that the count of teams involved changes within a specified range or across different instances or conditions.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bde30ce08190ab60a181ad2d321d |
completed | March 1, 2026, 10:29 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6078088190ba0221ae3368416c |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bbf83584819088c69366f58586cc |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:46 p.m.