Triple
T38483271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Illinois Mr. Basketball |
E917840
|
entity |
| Predicate | hasOppositeGenderAward |
P133773
|
FINISHED |
| Object | Illinois Ms. Basketball |
—
|
NE NERFINISHED |
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: Illinois Ms. Basketball | Statement: [Illinois Mr. Basketball, hasOppositeGenderAward, Illinois Ms. Basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOppositeGenderAward Context triple: [Illinois Mr. Basketball, hasOppositeGenderAward, Illinois Ms. Basketball]
-
A.
awardCategoryGender
Indicates that an award category is designated for recipients of a specific gender.
-
B.
hasOppositeAward
chosen
Indicates that one award is defined as the opposite or counterpart of another award.
-
C.
honoreeGender
Indicates the gender associated with the person who is being honored in the given context.
-
D.
hasOppositeTypeAward
Indicates that an entity has received an award that is the opposite in type or category to another related award.
-
E.
winnerGender
Indicates the gender of the entity that is the winner in a given event or competition.
- 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_69f76e9894208190a129a553a60ca58c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd76d1e5208190a6f26651492d1e3c |
completed | May 8, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69fd702a226c81908edfda00f4be4130 |
completed | May 8, 2026, 5:10 a.m. |
Created at: May 3, 2026, 4:31 p.m.