Triple
T33280147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Household of Umm Salama |
E852019
|
entity |
| Predicate | genderDynamic |
P119314
|
FINISHED |
| Object | prominent role of a Muslim woman in the household |
—
|
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: prominent role of a Muslim woman in the household | Statement: [Household of Umm Salama, genderDynamic, prominent role of a Muslim woman in the household]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderDynamic Context triple: [Household of Umm Salama, genderDynamic, prominent role of a Muslim woman in the household]
-
A.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
B.
genderSpecificity
chosen
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
C.
genderCustom
Indicates that an entity has a user-specified or non-standard gender designation beyond predefined gender categories.
-
D.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
-
E.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
- 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_69f349653da08190819876015a298fdb |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6e02ba6b881908dfafc52d3b75f1c |
completed | May 3, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69f6de09c2f481909f8b2545d3208c9f |
completed | May 3, 2026, 5:32 a.m. |
Created at: May 1, 2026, 1:32 a.m.