Triple
T431909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke of Normandy |
E9730
|
entity |
| Predicate | genderNeutralUsage |
P6042
|
FINISHED |
| Object | British monarch is styled Duke of Normandy regardless of sex |
—
|
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: British monarch is styled Duke of Normandy regardless of sex | Statement: [Duke of Normandy, genderNeutralUsage, British monarch is styled Duke of Normandy regardless of sex]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderNeutralUsage Context triple: [Duke of Normandy, genderNeutralUsage, British monarch is styled Duke of Normandy regardless of sex]
-
A.
genderNeutralForm
chosen
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
B.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
C.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
D.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
E.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eef07e748190b05392778f3de980 |
completed | Feb. 28, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69a2edd9264c8190b92f9a50348e5541 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.