Triple
T25637266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USC Trojans athletics |
E642738
|
entity |
| Predicate | NCAAChampionshipsTotalApproximate |
P24536
|
FINISHED |
| Object | over 100 team national championships |
—
|
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: over 100 team national championships | Statement: [USC Trojans athletics, NCAAChampionshipsTotalApproximate, over 100 team national championships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NCAAChampionshipsTotalApproximate Context triple: [USC Trojans athletics, NCAAChampionshipsTotalApproximate, over 100 team national championships]
-
A.
NCAAChampionshipCount
chosen
Indicates the number of NCAA championships an entity has won.
-
B.
NCAANationalChampionshipYearsApprox
Indicates the approximate years in which an entity won the NCAA national championship.
-
C.
NCAAtournamentChampionshipYears
Indicates the years in which an entity won the NCAA tournament championship.
-
D.
nationalChampionshipParticipation
Indicates that an entity has taken part in a national-level championship competition.
-
E.
consecutiveNCAAChampionships
Indicates that the subject has won NCAA championships in consecutive years, with the object specifying the number of such back-to-back titles.
- F. None of above.
Provenance (3 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_69e77e7ce28081908b08d65ee6e5c8be |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5fa6345548190a52498ecb0a2f555 |
completed | May 2, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69f4807f8680819098a524158d049c63 |
completed | May 1, 2026, 10:29 a.m. |
Created at: April 21, 2026, 5:34 p.m.