Triple
T4953832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilton Abbey |
E111232
|
entity |
| Predicate | genderOfCommunity |
P72
|
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: [Wilton Abbey, genderOfCommunity, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfCommunity Context triple: [Wilton Abbey, genderOfCommunity, female]
-
A.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
B.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
sexOrGender
chosen
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
D.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
E.
hasTypicalGenderAssociation
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
- 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_69bd4418390c8190b7e9766a2512ce55 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd71b82dd88190adfb08c3b3191fe0 |
completed | March 20, 2026, 4:11 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3aa1388190b3e0c8ee1ba1e4fa |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.