Triple
T13548883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Yacht Squadron |
E323588
|
entity |
| Predicate | hasGenderPolicyHistory |
P277
|
FINISHED |
| Object | historically male-only |
—
|
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: historically male-only | Statement: [Royal Yacht Squadron, hasGenderPolicyHistory, historically male-only]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderPolicyHistory Context triple: [Royal Yacht Squadron, hasGenderPolicyHistory, historically male-only]
-
A.
hasGenderPolicy
chosen
Indicates that an entity has adopted, implemented, or is governed by a specific policy related to gender issues or gender equality.
-
B.
hasGenderHistory
Indicates that an entity has undergone or experienced a change or transition in gender over time.
-
C.
hadGender
Indicates that an entity possessed a specific gender.
-
D.
hasGenderDivisions
Indicates that something is organized, classified, or separated into groups based on gender.
-
E.
governsGender
Indicates that one entity determines or constrains the gender classification or gender-related properties of another entity.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.