Triple
T34382550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grameen Bank |
E882475
|
entity |
| Predicate | percentageOfWomenBorrowers |
P139120
|
FINISHED |
| Object | high proportion of women clients |
—
|
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: high proportion of women clients | Statement: [Grameen Bank, percentageOfWomenBorrowers, high proportion of women clients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: percentageOfWomenBorrowers Context triple: [Grameen Bank, percentageOfWomenBorrowers, high proportion of women clients]
-
A.
hasGenderRatioFemale
chosen
Indicates the proportion or percentage of females relative to the total population in the described group or context.
-
B.
memberCountFemale
Indicates the number of female members associated with a given group or entity.
-
C.
womenStatus
Indicates the social, legal, economic, or cultural position or condition assigned to women within a given context or system.
-
D.
genderRatio
Indicates the proportional relationship between different genders within a given group or population.
-
E.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
- 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_69f349c0219881909393bbbc1edc8161 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f727bde8f88190ad746ca515134ca1 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72739c30c81908642eef3feb3afcf |
completed | May 3, 2026, 10:45 a.m. |
Created at: May 1, 2026, 1:59 a.m.