Triple
T36010486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duchess of Rutland |
E1041391
|
entity |
| Predicate | femaleEquivalentOf |
P158000
|
FINISHED |
| Object | Duke of Rutland |
—
|
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: Duke of Rutland | Statement: [Duchess of Rutland, femaleEquivalentOf, Duke of Rutland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleEquivalentOf Context triple: [Duchess of Rutland, femaleEquivalentOf, Duke of Rutland]
-
A.
maleEquivalent
Indicates that one entity is the corresponding male counterpart or equivalent of another entity.
-
B.
femaleCounterpartOf
chosen
Indicates that one entity is the female equivalent or corresponding counterpart of another entity within a given role, relationship, or category.
-
C.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
D.
genderCounterpartOf
Indicates a relationship where one entity is the corresponding counterpart of another with respect to gender.
-
E.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
- 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_69f76e2a02208190aedd1f9025a8b300 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7acb398d481909a1e7fecf76c4035 |
completed | May 3, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f7ab75387c819091afc3c2128eb903 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.