Triple
T34288565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houris |
E879812
|
entity |
| Predicate | genderInClassicalSources |
P20413
|
FINISHED |
| Object | primarily 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: primarily female | Statement: [Houris, genderInClassicalSources, primarily female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderInClassicalSources Context triple: [Houris, genderInClassicalSources, primarily female]
-
A.
genderInOldNorse
Indicates the grammatical gender that a given entity has in the Old Norse language.
-
B.
hasGenderInSomeTraditions
chosen
Indicates that, in at least some cultural, religious, or historical traditions, the subject is regarded as having a specific gender.
-
C.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
- 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_69f349b6df1c81908e5e5b6c2ab6409b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71fb1ab3881908e2f7c0e6f23db49 |
completed | May 3, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f71cc6397881909aaad37a9daa8a7e |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 1:57 a.m.