Triple
T19014917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarence "Big House" Gaines |
E465321
|
entity |
| Predicate | totalCollegeBasketballLosses |
P39703
|
FINISHED |
| Object | 447 |
—
|
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: 447 | Statement: [Clarence "Big House" Gaines, totalCollegeBasketballLosses, 447]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCollegeBasketballLosses Context triple: [Clarence "Big House" Gaines, totalCollegeBasketballLosses, 447]
-
A.
coachingRecordCollegeLosses
chosen
Indicates the number of games a coach’s college team has lost in their coaching record.
-
B.
mostGamesLostBy
Indicates that one entity holds the record for having lost the greatest number of games to another entity.
-
C.
seasonRecordLosses
Indicates the number of games a team lost during a specific season.
-
D.
careerLosses
Indicates the total number of defeats or losses an entity has accumulated over the course of its entire career.
-
E.
regularSeasonLosses
Indicates the number of games a team lost during the regular season portion of a competition or league.
- 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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6d9f2a081908c0e923809da88e2 |
completed | April 20, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69e4a2fd80c081908237317a3a883e1c |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:02 p.m.