Triple
T28155188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ladies’ Plate (original title) |
E714722
|
entity |
| Predicate | genderInTitle |
P1805
|
FINISHED |
| Object | women |
—
|
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: women | Statement: [Ladies’ Plate (original title), genderInTitle, women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderInTitle Context triple: [Ladies’ Plate (original title), genderInTitle, women]
-
A.
hasGenderedTitle
chosen
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
-
B.
usedBothMaleAndFemaleTitles
Indicates that an entity has been referred to or addressed using both male and female honorifics or titles.
-
C.
oppositeTitleByGender
Indicates that one title is the gender-based counterpart of another title (e.g., king/queen, actor/actress).
-
D.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
E.
titleHolderSex
Indicates the biological or identified sex of the person who holds a particular title.
- 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_69efd6b156448190bfa15958208395c3 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 10:02 p.m.