Triple
T27633427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UCA Bears |
E696403
|
entity |
| Predicate | womenNickname |
P130996
|
FINISHED |
| Object | Sugar Bears |
—
|
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: Sugar Bears | Statement: [UCA Bears, womenNickname, Sugar Bears]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: womenNickname Context triple: [UCA Bears, womenNickname, Sugar Bears]
-
A.
femaleAbbreviation
Indicates that one entity is an abbreviation or shortened form specifically denoting a female version of another entity.
-
B.
femaleCommonName
Indicates that the associated name is commonly used as a given name for females.
-
C.
usesNicknameForWomen'sTeams
chosen
Indicates that an organization or context refers to its women’s sports teams using a distinct nickname (often different from or modified relative to the men’s teams’ nickname).
-
D.
adultFemaleName
Indicates that the associated name is used for an adult female person.
-
E.
femaleCounterpartOf
Indicates that one entity is the female equivalent or corresponding counterpart of another entity within a given role, relationship, or category.
- 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_69ef59092c8881908114ad184248cc46 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f63125efdc819082d61f5528dcb5b4 |
completed | May 2, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69f62c1921008190a62675a31f66a875 |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 2:22 p.m.