Triple
T18926434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Hill Medal |
E462983
|
entity |
| Predicate | namesakeGender |
P27732
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Dorothy Hill Medal, namesakeGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namesakeGender Context triple: [Dorothy Hill Medal, namesakeGender, female]
-
A.
namedForGender
chosen
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
B.
namesakeDescription
Indicates that the object provides a descriptive explanation of why or how the subject is considered a namesake of something or someone.
-
C.
namesakeType
Indicates the specific kind or category of namesake relationship that exists between two entities (for example, one being named after the other as a person, place, event, or object).
-
D.
nameGenderInJapaneseContext
Indicates that a given name is associated with a particular gender within Japanese cultural and linguistic conventions.
-
E.
genderOfName
Indicates the gender typically associated with a given name.
- 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_69d8dcfdbbb881909964fa5a75bd0b48 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c9bc36588190ae9cc3b8abf8afd4 |
completed | April 20, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69e4a2e9e6488190ba8df92c8058ed88 |
completed | April 19, 2026, 9:39 a.m. |
Created at: April 10, 2026, 11:59 a.m.