Triple
T2450288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michigan Wolverines men's basketball |
E53686
|
entity |
| Predicate | hasNationalRunnerUpFinishes |
P39931
|
FINISHED |
| Object | multiple NCAA championship game appearances |
—
|
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: multiple NCAA championship game appearances | Statement: [Michigan Wolverines men's basketball, hasNationalRunnerUpFinishes, multiple NCAA championship game appearances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNationalRunnerUpFinishes Context triple: [Michigan Wolverines men's basketball, hasNationalRunnerUpFinishes, multiple NCAA championship game appearances]
-
A.
runnerUpCountry
Indicates the country that finished in second place in a competition or ranking.
-
B.
runnerUp
Indicates that one entity finished in second place relative to another in a competition or ranking.
-
C.
notablePlayerRunnerUp
Indicates that the referenced player was a runner-up (finished in second place) in a notable competition or event.
-
D.
runnerUpScore
Indicates the score achieved by the participant or entity that finished in second place in a competition or ranking.
-
E.
runnerUpLeague
Indicates that an entity finished in second place in a league competition.
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd2bc7b5481908b3664495e99f1a4 |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0aed2688190a18fe66b98d80e0b |
completed | March 7, 2026, 7:15 a.m. |
| PDg | Predicate description generation | batch_69abd2baee308190bdaa41ef1f6bc9cc |
completed | March 7, 2026, 7:24 a.m. |
Created at: March 6, 2026, 9:43 p.m.